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KARADENIZ TECHNICAL UNIVERSITY * INSTITUTE OF SOCIAL SCIENCES

DEPARTMENT OF WESTERN LANGUAGES AND LITERATURE

MASTER’S PROGRAM IN APPLIED LINGUISTICS

A CORPUS-BASED SEMANTIC PROSODIC ANALYSIS OF ENGLISH INTENSIFIERS

IN NATIVE AND NON-NATIVE CORPORA WITH A SPECIAL FOCUS TO THEIR

USAGE PATTERNS AND EVALUATIVE MEANINGS

MASTER'S THESIS

Neslihan KELEŞ

MAY - 2019

TRABZON

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KARADENIZ TECHNICAL UNIVERSITY * INSTITUTE OF SOCIAL SCIENCES

DEPARTMENT OF WESTERN LANGUAGES AND LITERATURE

MASTER’S PROGRAM IN APPLIED LINGUISTICS

A CORPUS-BASED SEMANTIC PROSODIC ANALYSIS OF ENGLISH INTENSIFIERS

IN NATIVE AND NON-NATIVE CORPORA WITH A SPECIAL FOCUS TO THEIR

USAGE PATTERNS AND EVALUATIVE MEANINGS

MASTER’S THESIS

Neslihan KELEŞ

Thesis Advisor: Asst. Prof. Dr. Ali Şükrü ÖZBAY

MAY - 2019

TRABZON

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DECLARATION OF ORIGINALITY

I, Neslihan KELEŞ, hereby confirm and certify that;

I am the sole author of this work and I have fully acknowledged and documented in

my thesis all sources of ideas and words, including digital resources, which have

been produced or published by another person or institution,

this work contains no material that has been submitted or accepted for a degree or

diploma in any other university or institution,

all data and findings in the work have not been falsified or embellished,

this is a true copy of the work approved by my advisor and thesis committee at

Karadeniz Technical University, including final revisions required by them.

I understand that my work may be electronically checked for plagiarism by the use of

plagiarism detection software and stored on a third party’s server for eventual future

comparison,

I take full responsibility in case of non-compliance with the above statements in

respect of this work.

21.05.2019

IV

ACKNOWLEDGEMENT

First and foremost, I want to express my sincere gratitude to my thesis supervisor, Asst. Prof.

Dr. Ali Şükrü ÖZBAY for his infectious enthusiasm, wisdom and expert guidance. I am also

thankful to him not only for supplying me with his own corpus, but also for providing immediate

feedback for my research.

I would like to thank the distinguished members of committee, Prof. Dr. Mehmet TAKKAÇ

and Asst. Prof. Dr. Öznur SEMİZ. I am also highly indebted to the academic staff from the

Department of English Language and Literature in Karadeniz Technical University: Assoc. Prof.

Dr. M. Zeki ÇIRAKLI, Assoc. Prof Dr. M. Naci KAYAOĞLU, Asst. Prof. Dr. Fehmi TURGUT

and Prof. Dr. İbrahim YEREBAKAN from whom I have learn a lot with their profound knowledge.

My best friend from university years, Elif AYDIN YAZICI deserves the special thanks for

her sincere motivation. I am sure that whenever I truly need help, she does her best without any

hesitation. We achieved our desired goal together.

I am grateful to Handan İLYAS KARATAŞ, Seyhan ÇAĞLAR ERDOĞAN, Mevlüde

ABDİOĞLU and Binnur OLGUN KAPTAN as my thesisters; an expression coming from the

bottom of my heart referring to ‘thesis sisters’. I have been lucky enough to collaborate with such

fellow researchers. I have also special thanks to my friends Nilgün MÜFTÜOĞLU, Zeynep

ÖZTÜRK DUMAN and Gökçenaz GAYRET for their moral and intellectual support.

My grateful thanks are also extended to Tuncer AYDEMİR for his precious help in using the

corpus tools.

Last but definitely not least, I heartily express my gratitude to my parents Ömer and Nurten

ÖZDEMİR for providing me with their patience and unfailing support throughout my years of

study. I owe my deep sense of appreciation to my husband Mehmet KELEŞ for his continuous

encouragement under all circumstances.

This thesis is dedicated to my precious daughters Mira and Hira from whose time I stole the

most.

May, 2019 Neslihan KELEŞ

V

CONTENTS

ACKNOWLEDGEMENT ............................................................................................................. IV

CONTENTS ...................................................................................................................................... V

ÖZET .............................................................................................................................................. VII

ABSTRACT .................................................................................................................................. VIII

LIST OF TABLES ......................................................................................................................... IX

LIST OF FIGURES ....................................................................................................................... XI

LIST OF ABBREVIATIONS ....................................................................................................... XII

INTRODUCTION ......................................................................................................................... 1-4

CHAPTER ONE

1. FRAMEWORK OF THE STUDY ......................................................................................... 5-11

1.1. Introduction .............................................................................................................................. 5

1.2. Background of the Study .......................................................................................................... 5

1.3. Statement of the Problem ......................................................................................................... 7

1.4. Purpose and Significance of the Study ..................................................................................... 8

1.5. Research Questions ................................................................................................................ 10

1.6. Operational Definitions .......................................................................................................... 10

1.7. Outline of the Study ............................................................................................................... 11

CHAPTER TWO

2. LITERATURE REVIEW ..................................................................................................... 12-45

2.1. Introduction ............................................................................................................................ 12

2.2. The Importance of Corpus Linguistics in Understanding Language ..................................... 12

2.3. Theoretical Background of the Study .................................................................................... 14

2.3.1. Contrastive Interlanguage Analysis (CIA) ................................................................... 15

2.3.1.1. What is Interlanguage? .................................................................................... 16

2.3.1.2. Historical Overview of Contrastive Intelanguage Analysis ............................. 21

2.3.1.3. Studies on Conrastive Interlanguage Analysis ................................................ 23

2.3.2. Computer Learner Corpus (CLC) Research ................................................................ 25

2.3.2.1. The Use of Learner Corpora in SLA and EFL Research ................................. 27

VI

2.3.2.2. Corpus Compilation and Design Criteria ......................................................... 28

2.3.2.3. Reference Corpora ........................................................................................... 29

2.3.3. Semantic Prosody (SP) ................................................................................................ 30

2.3.3.1. Semantic Prosody as an Extended Unit of Meaning (EUM) ........................... 34

2.3.3.2. Semantic Preference vs. Semantic Prosody ..................................................... 35

2.3.3.3. Studies Related to Semantic Prosody ............................................................. 37

2.4. Intensifiers .............................................................................................................................. 39

2.4.1. Amplifiers ..................................................................................................................... 40

2.4.2. Studies on the use of Intensifiers by EFL Learners ...................................................... 41

CHAPTER THREE

3. METHODOLOGY ................................................................................................................ 46-52

3.1. Introduction ............................................................................................................................ 46

3.2. Methodological Framework ................................................................................................... 46

3.2.1. Labelling of Patterns .................................................................................................. 48

3.3. The Selection Criteria of Intensifiers ..................................................................................... 49

3.4. Corpora and Tools for Data Collection .................................................................................. 49

3.4.1. Learner Corpora ........................................................................................................... 49

3.4.2. Reference Corpus ......................................................................................................... 50

3.4.3. Corpus Tools ................................................................................................................ 51

3.5. Data Analysis Procedures ...................................................................................................... 52

CHAPTER FOUR

4. FINDINGS AND DISCUSSION .......................................................................................... 53-83

4.1. Introduction ............................................................................................................................ 53

4.2. Overall Frequency Distribution of Amplifiers ....................................................................... 53

4.2.1. Maximizers .................................................................................................................. 54

4.2.2. Boosters ....................................................................................................................... 59

4.3. Semantic Prosodic Description of Amplifiers ....................................................................... 64

4.3.1. Semantic Profiles of Maximizers ................................................................................. 65

4.3.2. Semantic Profiles of Boosters ...................................................................................... 72

4.4. Pattern Analysis of Amplifiers .............................................................................................. 74

4.4.1. Usage Patterns of Maximizers ..................................................................................... 74

4.4.2. Usage Patterns of Boosters .......................................................................................... 78

4.4.2.1. Very ................................................................................................................. 78

4.4.2.2. So ..................................................................................................................... 80

4.4.2.3. Too ................................................................................................................... 83

VII

CONCLUSION AND SUGGESTIONS ........................................................................................ 85

REFERENCES ................................................................................................................................ 90

APPENDICES ............................................................................................................................... 101

CURRICULUM VITAE ............................................................................................................... 120

VIII

ÖZET

Bu tezin amacı İngilizce’de yer alan pekiştireçlerin ana dili İngilizce ve ana dili Türkçe olan

üniversite seviyesindeki yabancı dil öğrencileri tarafından tartışmacı yazılar içerisindeki kullanım

sıklığını, kullanım şekillerini ve anlamlarını yerli ve yabancı derlem verilerine dayalı olarak

anlamsal bürün (semantik prozodi) açısından incelemektir. Pekiştireçler, yazılı anlatımdaki

ifadeleri güçlendiren ve vurgulayan zarf niteliğinde sözcüklerdir. Son yıllarda dil bilimi alanında

birçok çalışmaya konu olan semantik prozodi kavramı ise dil birimlerinin ve sözcük gruplarının

örtülü biçimde taşıdığı olumlu olumsuz veya nötr anlam yüklerinin belli bir semantik ortamda

kullanılma eğilimi olarak tanımlanmaktadır. Bu çalışma metodolojik açıdan karşılaştırmalı ara dil

analizini benimsemektedir. Bu konuda yapılan araştırmaların ancak bilgisayar destekli derlem

(corpus) verileri ile objektif bir şekilde incelenebilmesi mümkün olabilmektedir. Bu bağlamda,

internet destekli derlem arayüzü olarak Sketch Engine programı kullanılmış olup çalışmaya konu

olan hedef pekiştireçler üç derlemin karşılaştırılması ile anlamsal bürün açısından incelenmiştir.

KTUCLE, TICLE ve LOCNESS derlemleri çalışmamızda temel alınan yerli ve yabancı derlem

kaynakları olarak kullanılmıştır. KTUCLE İngiliz Dili ve Edebiyatı öğrencilerinin yazma (writing)

dersinden elde edilen 709,748 kelimelik bir öğrenici derlemidir. Uluslararası Öğrenici Derlemi

(ICLE)’nin bir alt derlemi olan 199,532 kelime içerikli TICLE çalışmamızdaki ikinci öğrenici

derlemidir. Araştırmamızın referans derlemi olan LOCNESS ise anadili İngilizce olan öğrencilerin

makalelerinden elde edilen 361,054 kelimeden oluşan genel bir derlemdir. Verilerin incelenmesi

neticesinde üniversite düzeyindeki Türk yabancı dil öğrencilerinin kısıtlı pekiştireçleri kullandıkları

ve bu pekiştireçlerin anlamsal bürün ve eşdizim açısından farkındalık düzeylerinin düşük olduğu

ortaya çıkmıştır. Ayrıca ana dili İngilizce olmayan öğrencilerin kullanım ve anlam bakımından

birbirine yakın pekiştireçleri sıkça kullanmayı tercih ettikleri görülmektedir. Bu çalışmanın önemi

yerli derlemlerin yabancı derlemlerle karşılaştırılarak incelenmesi neticesinde anadili İngilizce

olmayan öğrencilerin hedef dilde yazmada pekiştireçleri kullanım şekilleri ve anlam bakımından

farkındalıkları yabancı dilde kelime öğrenimine pedagojik açıdan ışık tutmaktır.

Anahtar Kelimeler: Anlamsal Bürün, Pekiştireçler, Tartışmacı Yazı, Öğrenici Derlemi

IX

ABSTRACT

The primary purpose of the present study is to investigate semantic prosodic nature of

intensifiers used by both native speakers of English and Turkish students of English as a Foreign

Language (EFL) learners in their expository and argumentative essays along with a special focus

on their overall distribution and usage patterns. Intensifiers are degree adverbials modifying

attitudinal or evaluative meaning in written production. Semantic prosody, on the other hand, refers

to a hidden meaning, either positive, negative or neutral, which is revealed when certain node

words frequently collocate with other words from different semantic sets. The study mainly adopts

the methodology of Contrastive Interlanguage Analysis by Granger (1996) in nature. A

comparative corpus research on the semantic prosodic analysis of intensifiers can simply and

objectively be conducted with the help of special corpus tools in a computerized environment. In

this regard, the study investigating the semantic prosodic analysis of ten target intensifiers (the

maximizers absolutely, completely, entirely, fully, perfectly, totally, and utterly as well as the

boosters very, so, and too) utilizes Sketch Engine as the concordancer to compare three distinct

corpora; KTUCLE (Karadeniz Technical University Corpus of Learner English) together with

TICLE for non-native corpora and LOCNESS (Louvain Corpus of Native English Essays) for

native corpus. The local learner corpus of the study, KTUCLE, contains essays written by the

students of a Turkish university called Karadeniz Technical University and consists of 709,749

words. The second learner corpora is TICLE, as Turkish sub-corpus of ICLE (International Corpus

of Learner English), comprises 223,449 tokens in total. It is a collection of argumentative essays

written by Turkish adult learners of English. The reference corpus called LOCNESS contains

essays of native speakers and includes a total of 361,054 words. The results of the data analysis

concludes that tertiary level EFL learners use rather limited range of maximizers such as

completely and totally, and they have little semantic prosodic awareness about their usage. They

predominantly prefer using boosters such as very which are open-ended in use and can be

interchangeably used in all written discourse. This study is significant for revealing semantic

prosodic behavior of non-native EFL learners concerning intensifier use and providing pedagogical

implications for phraseological skills in foreign language learning.

Keywords: Semantic Prosody, Intensifiers, Argumentative Writing, Learner Corpus

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LIST OF TABLES

Table No. Name of Table Page No.

1. Learner Corpus Design Criteria ..................................................................................... 29

2. Examples of Semantic Prosody ..................................................................................... 34

3. The Components of EUM ............................................................................................... 35

4. Different Categorizations of Intensifiers ........................................................................ 40

5. Partington’s Study on Intensifiers................................................................................... 43

6. The CIA Model of the Study .......................................................................................... 47

7. The Profiles of the Three Corpora Utilized in the Research ........................................... 47

8. Selected Intensifiers ........................................................................................................ 49

9. The Design Criteria of KTUCLE ................................................................................... 50

10. The Design Criteria of TICLE ........................................................................................ 50

11. The Profile of LOCNESS ............................................................................................... 51

12. Overall Distribution of Maximizer + Adjectives ............................................................ 54

13. 1R + Adjective Collocations of completely .................................................................... 55

14. 1R + Adjective Collocations of totally ........................................................................... 56

15. 1R + Adjective Collocations of absolutely ..................................................................... 56

16. 1R + Adjective Collocations of perfectly ....................................................................... 57

17. 1R + Adjective Collocations of entirely ......................................................................... 57

18. 1R + Adjective Collocations of fully .............................................................................. 58

19. 1R + Adjective Collocations of utterly ........................................................................... 58

20. Overall Distribution of Booster + Adjectives ................................................................. 59

21. Log-likelihood Ratio of very in LOCNESS and KTUCLE ............................................ 59

22. Log-likelihood Ratio of very in LOCNESS and TICLE ................................................. 60

23. Log-likelihood Ratio of so in LOCNESS and KTUCLE ................................................ 61

24. Log-likelihood Ratio of so in LOCNESS and TICLE .................................................... 62

25. Log-likelihood Ratio of too in LOCNESS and KTUCLE .............................................. 63

26. Log-likelihood Ratio of too in LOCNESS and TICLE ................................................. 64

27. Semantic Prosodic Profiles of Maximizers + Adjectives ............................................... 65

28. The Semantic Prosodic Profile of absolutely .................................................................. 66

29. The Semantic Prosodic Profile of completely ................................................................. 67

30. The Semantic Prosodic Profile of entirely ...................................................................... 68

31. The Semantic Prosodic Profile of fully ........................................................................... 69

XI

32. The Semantic Prosodic Profile of perfectly .................................................................... 70

33. The Semantic Prosodic Profile of totally ........................................................................ 70

34. The Semantic Prosodic Profile of utterly ........................................................................ 71

35. Semantic Prosodic Profiles of Booster + Adjectives ..................................................... 72

36. Overall Percentages of Maximizer Patterns ................................................................... 74

37. Typical Patterns of ‘INT-adj’ in LOCNESS .................................................................. 76

38. Typical Patterns of ‘INT-adj’ in KTUCLE ..................................................................... 77

39. Typical Patterns of ‘INT-adj’ in TICLE ......................................................................... 77

XII

LIST OF FIGURES

Figure No. Name of Figure Page No.

1. Contrastive Interlanguage Analysis ................................................................................ 15

2. Interlanguage .................................................................................................................. 17

3. The Five Mental Processes of Interlanguage .................................................................. 18

4. Varieties of English ........................................................................................................ 26

5. The Subsets of Intensifiers .............................................................................................. 40

XIII

LIST OF ABBREVIATIONS

BNC : British National Corpus

CIA : Contrastive Interlanguage Analysis

CLC : Contrastive Interlanguage Analysis

EA : Error Analysis

EFL : English as a Foreign Language

IL : Interlanguage

KTUCLE : Karadeniz Technical University Corpus of Learner English

L1 : First Language

LL : Log-likelihood Ratio

LOCNESS : Louvain Corpus of Native English Essays

NL : Native Language

NNS : Non-native Speakers

NS : Native Speakers

SLA : Second Language Acquisition

SP : Semantic Prosody

TICLE : Turkish International Corpus of Learner English

TL : Target Language

1

INTRODUCTION

English is “– the language - on which the sun does not set, whose users never sleep”

(Quirk, 1985: 1)

There has been an increase in the number of non-native speakers of English worldwide; even

they greatly outnumber native ones at present time. The emergence and spread of English as the

world’s first global language, namely as lingua franca, has fundamentally altered the focus of

linguistic studies paving the way for teaching and learning it in an efficient way. Foreign language

acquisition at tertiary level has been a controversial issue for decades in places where English is not

the first language. This fact has been certainly valid in Turkey where English language is the

medium of instruction in many universities today.

University students come across a wide range of challenges and adaptations, many of which

involve even the process of learning a new language (Biber, 2006: 1). Most tertiary level students

as non-native learners of English as a Foreign Language (henceforth, EFL) may not been furnished

with an adequate theoretical knowledge on the target language, so they are likely to encounter some

challenges in putting what they have learned into practice. In other words, even advanced level of

EFL learners may not be well-equipped with vocabulary and grammar rules; hence, they may not

be capable of achieving a proficiency and accuracy in writing skills. According to Pawley and

Syder (1983), the sentences that they generate can be acceptable and correct in terms of grammar,

but they may lack “idiomaticity” and “nativeness” (as cited in Wang, 2017: 3). More specifically,

Lorenz (1998: 53) claimed that advanced learners are typically considered as ‘advanced’ for being

able to command the “basic rules of syntax and morphology”. The students’ diversion from the

native-like usage, particularly in written production, creates an impact of foreign sounding or leads

to “lack of idiomaticity” (Lorenz, 1998: 53).

It is obvious that while acquiring a new language, the EFL learners may inevitably

experience difficulty in putting words together in a meaningful way. This is considerably because a

good command of vocabulary has the central place in language learning process. As Levelt (1989:

181) puts forward, “the lexicon is the driving force behind sentence production.” According to

Meara (1980: 221), English learners unhesitatingly accept the truth that they have serious problems

in learning vocabulary. After overcoming the early stages of foreign language learning process,

almost all of them agree on the point that the vocabulary acquisition still remains a difficult area to

learn. Undoubtedly, writing, as a productive language skill, can be regarded as one of the most

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problematic ways of language use by learners. Thus, the importance of mastering vocabulary in

writing has entailed more involvement of both EFL learners and researchers in this problematic

area.

The ability to write in foreign language is becoming increasingly fundamental, especially for

academic purposes; thus, productive knowledge of tertiary level EFL learners is considered to be

worth of further investigation through corpus linguistics. In other saying, it is emphasized by Leech

(1998: xviii) that according to most of the EFL learners, writing is an extremely significant

competency, and it is valuable for creating corpora from written learner language. In writing, the

potential problems and errors are inevitable with a limited amount of English that the language

learners have acquired during their interlanguage development. Thus, much attention is required to

be given to interlanguage development level of EFL learners so as to throw light on their awareness

of language use, especially in writing. The term “interlanguage (IL)” can be comprehended easily

when it is considered as a continuing process between first language and foreign language that the

language learners take part in actively and progress in time (Larsen-Freeman and Long, 1991: 60).

To understand the concept better, interlanguage can be defined as a state of language learning

process where target language (L2) is not completely acquired yet, and it still carries the traces of

mother tongue (L1).

One of the many ways eliciting the features of interlanguage is to investigate the awareness of

EFL learners on semantic prosody, which can be described in a way, in which a hidden meaning,

either positive, negative or neutral, is revealed when certain node words frequently collocate with

other words from different semantic sets. Partington (1998) highlights that it is important for non-

native learners to have knowledge of semantic prosody in order to understand “what is

grammatically possible in their language production” as well as which is proper to use and what

really occurs (as cited in Fuqua, 2014: 79). Furthermore, being aware of semantic prosodic aspect

of a language helps language learners and interpreters to make a distinction among synonymous

words (Morley and Partington, 2009: 140). According to Chief et al. (2000), many language users

or learners have to select certain words between lexical options which are provided by context or

their own mental lexicon, either consciously or unconsciously. The choice of the most contextually

appropriate word may not be easy for near-synonymous ones in terms of nuances and collocational

restrictions (as cited in Kara, 2017: 97). Intensifiers are among these near-synonymous words with

which foreign language learners frequently come across in daily life and struggle to choose

correctly between these items having nearly the same meaning. For Biber et al. (1999), the use of

English intensifiers differs in spoken and written production, and the frequency of intensifiers is

found to be more abundant in writing than in speaking (as cited in Dong and Chiu, 2013: 32). In

this sense, this particular corpus-based study concentrates upon semantic prosodic analysis of

intensifiers in Turkish learners’ academic writing and attempts to investigate whether there are any

influences of interlanguage development on their intensifier use.

3

Intensifiers in current English belong to a group of words typically functioning as adverb or

adverbials. Portner (2006: 149) states that there is a wide range of adverbials in world languages,

and it can be difficult to explain what the best way is to comprehend all of them. On the importance

of using intensifiers, Lobov (1984: 43) states that “at the heart of social and emotional expression is

the linguistic feature of intensity.” Partington (1993: 178) describes intensification as “a direct

indication of a speaker’s desire to use and exploit the expression of hyperbole.” In addition, he

underlines the significance of intensification for a successful conversation by adding that

intensifiers are generally used for strengthening the messages by affecting, approving or even

insulting to affect the receivers’ perception.

The EFL learners use different kinds of intensifiers in writing. “Although syntactically

marginal, adjective intensification plays a major role in spoken and written interaction” (Lorenz,

1998: 53). Novice writers might know dictionary meaning of intensifiers in English language;

however, they may ignore all other functions or contextual use of them. Gong and Wu (2012: 4)

points out that conventional dictionaries are inadequate for presenting additional knowledge on

various “usages, collocations, and connotative meanings for lexical words.” Therefore, it remains a

problem for foreign language learners and interpreters to use the vocabulary of the target language

like a native speaker. Another problem concerning the EFL learners’ use of intensifiers in written

discourse is L1 transfer. For example, most of the English intensifiers having some nuances in

meaning have the same equivalence in Turkish language. Therefore, Turkish EFL learners may get

difficulty in making a selection among English intensifiers which sounds more proper in certain

semantic sets. Restraining themselves from using a wide range of intensifiers in writing, they tend

to write with a limited set of intensifiers like very in order to eliminate the potential of making

mistakes. It is quite necessary to gain better insight into underlying problems of non-native learners

of English language while using intensifiers in their written essays, whether they overuse or

underuse them, or they neglect semantic prosodic nature of intensification.

The best and most convenient way to carry out an elaborative semantic prosodic analysis of

intensifiers can only be possible by a computerized corpus analysis. “It should be borne in mind

that semantic prosody is a relatively new concept and as such requires careful elaboration”

(Steward, 2010: 54). “The use of corpus for lexical investigation is not a recent phenomenon but its

full significance and value has, in the last decade, been realized especially after the introduction of

computerized corpus tools by a much larger group of linguists all around globe” (Özbay and

Kayaoğlu, 2016: 343). It is an undeniable truth that human intuition cannot be adequate alone for

disclosing semantic prosodic properties of certain word sets. Native speakers can sometimes make

accurate exemplifications on collocations, but they may not give precise estimations about their

frequency and overall distribution (Stubbs, 1995: 24). Hunston (2007) also claims that it would

hardly be possible to determine the prosodic profile of a word without the technological advances

in the methodology of corpus linguistics. Thanks to corpus linguistics, the study of semantic

4

prosody has become feasible even on larger language data. This corpus-based study aims at

investigating EFL learner’s semantic prosodic behavior while using English intensifiers in written

production in comparison to native speakers.

Apart from this introductory part, the current thesis has four main chapters that are

framework of the study, literature review, methodology, and findings and discussions as well as a

final part presenting conclusion and suggestions along with limitations, pedagogical implications.

5

CHAPTER ONE

1. FRAMEWORK OF THE STUDY

1.1. Introduction

The first chapter which is divided into seven subheadings presents the background of the

study, statement of the problem, significance and purpose of the study, main research questions,

operational definitions and outline of the thesis. The background part highlights the importance of

computerized learner corpora in corpus linguistics as well as Contrastive Interlanguage Analysis as

the research methodology of the thesis. Then, the main problem of the study is discussed in the

light of the research questions. Next part introduces the significance and aim of the research which

tries to investigate semantic prosodic analysis of intensifiers in terms of their usage patterns and

meanings based on native versus non-native corpora. Finally, the first chapter ends with the

operational definitions used in both theoretical and methodological background of the thesis, and it

outlines the research as a whole.

1.2. Background of the Study

Corpus linguistics, to a great extent, has been facilitating the studies on semantic prosody in

recent years. This explanation can be supported with McEnery and Wilson’s argument (2001: 1-2)

which asserts that corpus linguistics works on ‘real life’ evidences, and it is “a methodology rather

than an aspect of language requiring explanation or description.” In an attempt to illuminate corpus

linguistics further, it would be a wise step to touch upon the definition of ‘corpus’ in the first place.

For Atkins and Clear (1992), “a corpus is a body of text assembled according to explicit design

criteria for a specific purpose” (as cited in Granger, 1998: 7). As Francis (1982) defines, corpus is “a

collection of texts assumed to be representative of a given language, dialect, or other subset of a

language, to be used for linguistic analysis (as cited in Tognini-Bonelli, 2001: 53). Corpus linguistics,

which has gained much attention in language teaching area, is valued by Biber and Conrad (2001:

332) in the way that corpus is of high importance because it holds natural evidences in a context, and

it also provides a new insight with its quantitative analysis without which it could be otherwise

impossible for researchers to examine linguistic patterns.

One of the pioneers of corpus linguistics, Granger (1998: 3) asserts that computer has an

important role in corpus linguistics. She confirms that with the widespread use of the computer

6

corpus technology, a new discipline has appeared called ‘corpus linguistics’ which cannot be solely

regarded as a ‘computer-based methodology’, but it is a “new research enterprise, a new way of

thinking about language, which is challenging some of our most deeply-rooted ideas about

language” (Leech, 1992; as cited in Granger, 1998: 3). Granger (2004: 124) puts forward that

“computer learner corpora are electronic collections of spoken or written texts which are produced

by foreign or second language learners.” Aijmer (2002: 56) also claims that different aspects of

interlanguage, which has been a rather difficult area for investigation before, can be analyzed with

the help of computer learner corpora. Similarly, Louw (1993) states that semantic prosody is a

linguistic feature which has been only investigated through computational methods to be able to

reveal its level of progress (as cited in Stewart, 2010: 80).

At the center of many studies utilizing computerized learner corpora lies the methodology of

Contrastive Interlanguage Analysis (CIA). This recent approach, which Granger (1998) calls CIA,

comprises the comparisons of both native speakers versus non-native speakers (NS / NNS) as well

as non-native speakers versus non-native speakers (NNS / NNS). Granger (1998: 13) suggests that

native language and interlanguage (NL / IL) comparisons aim to uncover the features of “non-

nativeness” of learner language whereas the main objective of the comparison of two

interlanguages (IL / IL) is to hold a mirror to the nature of interlanguage. As Barlow (2005) notes,

some concerns occur when a corpus of learner is compared to a corpus of native speakers. The

researcher additionally argues that studies utilizing native corpora as a norm to compare with

learner corpora reflects “the nature of interlanguage” by highlighting the underlying aspects of non-

native speech or writing (Barlow, 2005: 342). Correspondingly, Leech (1998) states that “a

comparison of learner corpora with NS corpora provides data on the properties of interlanguage,

covering features which are typically overused or underused, in addition to those which are

misused by language learners” (as cited in Barlow, 2005: 342).

Among many interlanguage problems that EFL learners have recently encountered in

language learning process is semantic prosody, and it has become an increasingly important and

popular field of linguistic studies in the last two decades. As Zhang (2010: 190) asserts, “the notion

of semantic prosody was primarily introduced by Louw in 1993.” Additionally, Stubbs (1996)

suggests that semantic prosodies are divided into three categories: “some words tend to have a

predominantly negative prosody, a few have a positive prosody and many words are neutral” (as

cited in Wang, 2017: 15). As one of the fundamental fields of inquiry in corpus linguistics, this

study elaborates on semantic prosody roughly following Stubbs’s way of classification into three as

negative, positive or neutral.

The semantic prosody can be investigated through the use of intensification by non-native

learners because it seems a problematic area even for advanced EFL learners having a higher

proficiency in language (Lorenz, 1999). Different forms of intensifiers have been categorized in

7

reference books of English grammar as well as in earlier studies. Quirk et al. (1985: 445) identify two

subcategories of intensifiers: “amplifiers and downtoners.” Lorenz (1999: 24) states that “on the

whole Quirk et al.’s classification has, of course, been highly influential” since many researchers

investigating the intensification employ Quirk et al.’s category in their studies (Partington, 1993;

Lorenz, 1999; Méndez-Naya, 2003; Wang, 2017, etc.). Therefore, the present study adopts the

categorization of Quirk et al. (1985) as the basis of analyzing English intensifiers.

As a significant lexical class in terms of strengthening meaning, intensifiers can also be

examined from different aspects, such as usage pattern and evaluative meaning. For Hunston and

Francis (2000: 3), “a pattern is a phraseology frequently associated with (a sense of) a word,

particularly in terms of the prepositions, groups, and clauses that follow the word.” In a broader

sense, they emphasize the interdependency of meaning and patterns with the explanation that “in

many cases different senses of words are distinguished by their typical occurrence in different

patterns; and secondly because words which share a given pattern tend also to share an aspect of

meaning” (Hunston and Francis, 2000: 3).

To sum, this comparative interlanguage analysis concentrates upon different usage patterns of

adjective intensification and their association with meaning from semantic prosodic point of view

by comparing non-native tertiary level EFL students with native speakers.

1.3. Statement of the Problem

Among four essential language skills, which are listening, speaking, reading, and writing, the

last one is commonly treated as the most challenging by EFL learners. The language learners do

not naturally acquire writing skill, but they learn and practice it over time. For certain, a language

being learnt is enhanced thanks to an adequate amount of exposure. That is to say, written

production in foreign language needs much time and effort in order to gain greater comprehension.

It is an absolute fact that writing in target language is a real concern for most learners.

Although the EFL students endeavor to memorize and learn the meaning of many isolated words,

they still have difficulty in employing them in an appropriate way. Conzett (2000) supports the

view that although learners of a foreign language might have the knowledge of many words or

grammar rules, they may be insufficient in using those words in combination with different

linguistic items; therefore, they may have inadequate competency to create a richer intended

meaning. In other saying, words may have different meanings in different semantic settings. Thus,

EFL learners should know how to use vocabulary and collocations in a context to express

themselves well.

8

In written English, intensification has “a potential to be a challenge for tertiary level EFL

learners since intensifiers may have synonymous meanings” (Özbay and Aydemir, 2017: 40). Biber

(1999) suggests that both in speaking and writing, one should make a selection among a great many

degree adverbials intensifying adjectives which can be used interchangeably like “fully and

‘strongly”, but “in many cases, there is little semantic difference between the degree adverbs. Thus

the adverbs could be exchanged in the following pair of sentences with little or no change of

meaning: That’s completely different. It’s totally different” (as cited in Kennedy, 2003: 470).

Before I was graduated as an English major, I had some concerns about using such adverbials to

put an emphasis or to add evaluative value to my written expressions. I had a great inclination to

use intensifiers in writing; however, I did not pay attention to their collocational nature and had

little semantic awareness about their usage patterns as an EFL learner. The wide range of

intensifiers that I had encountered in native writing samples made me stay focused on this issue as

an individual interest and preference for studying. In order to find out the nuances among near-

synonymous degree adverbials, I decided to conduct a corpus-based study dealing with the

semantic prosodic analysis of intensifiers in English language.

It is obvious that the learners of English language have an inclination to use intensifiers for

capturing readers’ attention on the intended message and to enhance the value of their writing skills.

Having a full command of the dictionary meaning of intensifiers may be insufficient for their

meaningful use. In a similar sense, Ahmadian et al. (2011) claim that learning the meaning of

individual words is not sufficient for acquiring fluency in L2. They argue that “knowing the way

words combine into chunks (collocations) characteristic of the language, as well as being aware of the

conditions of semantic prosody is necessary” (Ahmadian et al., 2011: 294). Concerning this truth, this

study mainly focuses on the semantic prosodic orientation of English intensifiers both in native and

learner language so as to reveal EFL learners’ potential to command their vocabulary knowledge and

skills in certain semantic discourses. Also, the study seeks to examine the role of degree adverbs in

English vocabulary and practicing them in writing skills. As Lorenz (1999: 27) suggests, intensifier

usage of target language learners can be investigated with the purpose of gaining an appreciated

understanding of behavior in target language. This will also contribute to raising EFL learners’

awareness of adverbials and intensification to achieving fluency and accuracy in English.

1.4. Purpose and Significance of the Study

The primary aim of this research work is to investigate semantic prosodic nature of

intensifiers used by both native speakers and Turkish EFL learners in their expository and

argumentative essays. Along with a special focus on usage patterns and meaning of intensifiers, the

study also tries to make a comparison between native and non-native learners regarding their

semantic prosodic awareness on adverbial use. The reason behind choosing the intensifiers as the

subject of study is that intensifiers are the kind of adverbials, which are frequently used by EFL

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learners in both oral and verbal language with an aim to add an emphasis or force to the meaning,

without paying attention to their positive or negative semantic prosodic features and collocational

usage patterns. In the scope of the study, it is contended that the mastery of these adverbials will

likely to increase the awareness of tertiary level EFL students towards the usage patterns of these

lexical items as well as their semantic prosodic analysis perspectives so that they make more

appropriate lexical decisions as well as consider the prosodic profiles.

From a broader perspective, ‘intensifier’ is used as an umbrella term covering all

intensification types, usage patterns and meanings encountered in the writings of both native and

non-native speakers of English. Although, ‘intensifiers’ are used as a general label on research title,

the focal point of the study is on a specific category of intensifiers, namely ‘amplifiers’. In this

study, the seven frequently used amplifiers selected from the studies of Kennedy (2003) and Wang

(2017) are comparatively analyzed concerning their types and usage patterns. The study tries to

find an answer whether the usage patterns have any effect on the meaning of intensifiers. All these

issues shed light on the semantic prosodic awareness of non-native learners versus native

intensification. In this context, this study aims to draw a detailed picture of the way Turkish EFL

learners use amplifiers having a position adjacently before adjectives in written language.

A comparative corpus research on the semantic prosodic analysis of intensifiers requires an

extensive investigation of large scale of learner corpora; thus, such studies can simply and

objectively be executed with the aid of special corpus tools or concordance programs in a

computerized environment. In an effort to carry out a contrastive corpus analysis, first and

foremost, a corpus needs to be compiled carefully because “the results are only as good as the

corpus” (Sinclair, 1991; as cited in Granger, 1998: 7). Therefore, a good compilation of a corpus is

the key element. Literally, the quality of the data adds great deal to the validity of the investigation

(Granger, 1998). Also, it is not always so easy to have an access to corpus of English learners

which is ready for analysis and freely available. Since I have no experience in teaching as a

profession, I have never had any chance to compile my own corpus. Therefore, with the guidance

of my supervisor, I have decided to heavily concentrate on a learner corpus called KTUCLE

compiled from the essays of tertiary level language students from Department of English Language

and Literature in Karadeniz Technical University.

The study is conducted by utilizing three distinct corpora; KTUCLE (Karadeniz Technical

University Corpus of Learner English) together with TICLE for non-native corpora and LOCNESS

(Louvain Corpus of Native English Essays) for native corpus. The central learner corpus of the

study, KTUCLE, contains essays written by the students of a Turkish university Karadeniz

Technical University and consists of 709,749 words. The second learner corpora is TICLE, as

Turkish sub-corpus of ICLE (International Corpus of Learner English), comprises 223,449 tokens

in total. It is composed of argumentative essays that were written by Turkish adult learners of

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English. The reference corpus called LOCNESS contains essays of native speakers of English and

includes a total of 361,054 words. The present study analyzes the different usage patterns of

intensifiers and their semantic prosodic nature in the two learner corpora; namely KTUCLE and

TICLE, comparing native speakers of English with reference to LOCNESS by using Sketch Engine

as a concordancer.

More specifically, the study which attempts to investigate the semantic prosodic awareness of

Turkish EFL learners on the use of intensifiers in their argumentative essays in English mainly

adopts the methodological approach of Contrastive Interlanguage Analysis (CIA), which includes

two kinds of comparison in nature as Granger (1998: 12) proposed: comparing native language and

interlanguage (NL vs. IL) and comparison of two distinct interlanguages (IL vs. IL). The purpose

of such a comparative research design is to identify the areas of language use that are resembling

and differentiating between native and non-native speakers of English in order to reveal common

interlanguage aspects of EFL learners. This research is conducted through the use of two learner

corpora: KTUCLE and TICLE as non-native learner corpora as well as LOCNESS for the control

corpus. The use of two different non-native learner corpora is most likely to provide information

about interlanguage features of the EFL learners.

1.5. Research Questions

The major focus of the recent study is on conducting a semantic prosodic analysis of

intensifiers in argumentative writing of Turkish EFL learners with a view to characterizing their

usage patterning and meaning features. The main research question inquires the semantic prosodic

awareness of Turkish EFL learners on the use of intensifiers in their written texts.

Also, this research tries to find answers to the following questions:

1. What is the frequency distribution of intensifiers in argumentative essays of tertiary level

Turkish EFL learners and native speakers of English in selected native and non-native

corpus?

2. Do intensifiers have a specific semantic prosodic profile such as positive, negative or

sometimes neutral as evidenced in the three corpora under investigation?

3. What are the different usage patterns of intensifiers existing in native corpus and non-

native corpora?

1.6. Operational Definitions

Concordancer: Text retrieval programs which are called ‘concordancers’ are among the

extensively used linguistic software devices (Granger, 1998: 14).

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Corpus: It is an extensive compilation of texts, either spoken or written, which are utilized

for research purposes (Collins Cobuild Dictionary, 2001: 313).

Corpus Linguistics: It is the study of large scale data related to language. Corpus linguistics

takes the advantage of computer in order to analyze huge amounts of transcribed nonverbal texts

(McEnery and Hardie, 2011).

Contrastive Interlanguage Analysis: Introduced by Granger (1998), CIA combines the two

major approaches: an IL vs. IL comparison and an IL vs NL comparison.

Intensifier: Words like very and extremely that are located before an adjective or adverb in

order to enrich its meaning is called ‘intensifier’ (Collins Cobuild Dictonary, 2001: 756)

Learner Corpora: Studies find ‘learner corpora’ helpful in second language acquisition as

they are useful in creating a learner language profile. They focus on error analysis or extensively or

rarely uttered words of the learner compared to native speakers (Baker, et al., 2006: 103).

Reference Corpus: Reference corpus aims to depict the general nature of the language via a

large scale corpus design rather than a sample of and specific written text (Baker, et al., 2006: 138).

Semantic Prosody: It is associated with lexical items collocating with semantic sets of words

that are either positive, negative or sometimes neutral (Stubbs, 1995).

Subcorpus: “A subset of a corpus, either a static component of a complex corpus or a

dynamic selection from a corpus during on-line analysis” (Atkins et al., 1992: 1).

1.7. Outline of the Study

This part presents the overall structure of the thesis which is consisted of five sections: as

follows:

Introduction: As the initial section of the study, it gives overall information and rationale

behind the thesis.

Chapter One: It presents the background information, the aim of the study, the significance

of the study as well as the statement of the problem along with research questions.

Chapter Two: It is the review of the literature which gives detailed information on

definitions of semantic prosody and intensifiers. Besides, it presents previous corpus-based

semantic prosodic studies in other countries related to use of intensifiers.

Chapter Three: It is the methodology part which presents the methods and procedures

employed in this study.

Chapter Four: It is the part of findings and discussion which introduces data obtained

through corpora and its elaborative analysis based on the research questions.

Conclusion and Suggestions: It presents an overview of the study as well as limitations,

pedagogical implications, and suggestions for further research.

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CHAPTER TWO

2. LITERATURE REVIEW

“I'm glad you like adverbs — I adore them;

they are the only qualifications I really much respect.”

Henry James (1902)

2.1. Introduction

This chapter reviews the relevant literature in the field of applied linguistics. From a general

perspective, this part initially highlights the importance of corpus linguistics in understanding

language and the use of corpora in linguistic investigations. Then, the chapter presents a theoretical

background discussing the major concepts of the study. As the core of the research, this chapter

also evaluates relevant studies on EFL learners’ use of intensifiers in written production with a

special focus to usage pattern and meaning.

2.2. The Importance of Corpus Linguistics in Understanding Language

Language is a system using signs and sounds to convey messages or meanings among the

members of society. A pioneer linguist Chomsky (2006: 88) claims that “when we study human

language, we are approaching what some might call the human essence, the distinctive qualities of

mind that are, so far as we know, unique to man.” Basically, the scientific study of this system

unique to human beings for communication is called linguistics. In addition to the aforementioned

views, “today the field of linguistics studies not just the nuts-and-bolts of forms and their

meanings”, but also the process of language learning, its role of creating interaction, the help of

computers in analyzing language, and the representation of language in human brains (Fasold and

Connor-Linton: 2006: 10).

There are numerous subfields of linguistics each dealing with another aspect of languages.

Corpus linguistic, one of these branches in applied linguistics, has started to develop rapidly in the

last three decades with the availability of computer technology in modern age. Aijmer and

Altenberg (1991: 1) claim that the reason of this development arises from two major cases that

occurred in the 1960s; the first one was launching of the Survey of English Usage (SEU) by

Randolph Quirk and the second event triggered by the development in computational area which

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paved the way for storing, scanning and grouping materials. The Brown corpus which is well-

known as “the first modern computerized corpus of the English language” was compiled by Nelson

Francis and Henry Kucera at Brown University (Aijmer and Altenberg, 1991: 1). From then on,

especially after 1980s, corpus linguistics gained popularity among scholars and linguistics

(McEnery et al., 2006: 4).

From an etymological point of view, “corpus is derived from the Latin word meaning body”

(Dash and Arulmozi, 2018: 4). Generally speaking, in linguistics, “the term corpus has been

historically stands for ‘body of data’ referring “a sample of utterances or texts – which provides

evidence about the language it comes from” (Cameron and Panovic, 2014: 81). However, they add

that nowadays in corpus linguistics, corpus is mostly used as a body of genuine linguistic input that

is kept in computational form and analyzed applying computational software. McEnery et al.

(2006: 5) summarized the scope of ‘corpus’ echoing the definitions of other scholars such as

Francis (1992) and Atkins et al. (1992); “…there is an increasing consensus that a corpus is a

collection of machine-readable (2) authentic texts (including transcripts of spoken data) which is

(3) sampled to be (4) representative of a particular language or language variety.”

According to Granger (2002: 4), corpus linguistics is a methodology of applied linguistics

which uses electronic linguistic data compiled from texts. She also argues that corpus is not a new

field in linguistics, but it is just the core of the linguistic evidence; thus, it happens to be a strong

methodology. For Kennedy (1998), corpus linguistics differs from other linguistic theories in terms

of the sources of evidence that CL provides (as cited in Şanal, 2007: 21):

Linguists have always needed sources of evidence for theories about the nature, elements,

structure and functions of language, and as a basis for stating what is possible in a language. At

various times, such evidence has come from intuition or introspection, from experimentation or

elicitation, and from descriptions based on observations of occurrence in spoken or written texts.

In the case of corpus-based research, the evidence is derived directly from texts. In this sense

corpus linguistics differs from approaches to language, which depend on introspection for

evidence.

In a similar sense, Altenberg (2011: xiii) holds the view that “although corpus linguistics is

strictly speaking a methodology rather than a theory of language, it has opened up new approaches

to the study of language and new and fruitful ways of matching theory and data.” As a linguist,

Sinclair (2004: 1) notes that working on corpora, a researcher may feel a sense of “creative energy”

which makes the studies applicable, subtle and flexible in nature. Startvik (2007: 23) agrees upon

the idea of Wallace Chafe (1992):

What, then, is a ‘corpus linguist’? I would like to think that it is a linguist who tries to

understand language, and behind language the mind, by carefully observing extensive natural

samples of it and then, with insight and imagination, constructing plausible understandings that

14

encompass and explain those observations. Anyone who is not a corpus linguist in this sense is,

in my opinion, missing much that is relevant to the linguistic enterprise.

Stubbs (1996) claims that “the heuristic power of corpus methods is no longer in doubt” (as

cited in Granger, 2002: 4). Not surprisingly, corpus linguistics enables the exploration of new

aspects of language and provides insights into the areas of language learning and teaching. It has

allowed scholars and linguists to explore many areas which could be difficult to investigate without

the advent of computers. According to Aijmer and Altenberg (1991: 2), the study of corpora

empowers the motivation of English language studies. When compared to old kinds of language

materials, corpus data provides more authentic evidence for the descriptive analysis of “lexis,

syntax, discourse and prosody” in English language. In the second place, corpus provides an

efficient basis for comparison of all aspects of English language in a probabilistic and quantitative

manner.

Apart from its benefits, the use of corpus has some limitations for linguistic studies as

summarized by Hunston (2002: 22-23). First of all, a corpus is thought to give information about

frequency, not possibility. The conclusions that are drawn from a corpus analysis should be

considered as inferences, not truths. In addition, a corpus can provide evidence instead of concrete

information. Lastly, a corpus reflects the aspects of a language “out of its context.”

As this section summarizes, the study of corpora plays an important role in understanding the

nature of language. When conducting a linguistic research, the data should have an objective base

to make an accurate interpretation about the language itself. Thus, corpus linguistics becomes an

inevitable part of language studies with the help of computer-assisted comparative analysis. Instead

of depending solely on the intuitions, the linguists or researchers can reach accurate numerical data

by comparing many aspects of different languages. With this in mind, this study adopts corpus

linguistics as the methodological basis to provide empirical evidences. The theories used in the

current corpus-based study will be explained in the next section.

2.3. Theoretical Background of the Study

The current study of corpus-linguistics investigates the semantic prosodic analysis of English

intensifiers by contrasting two computerized learner corpora with a reference corpus to elicit some

interlanguage problems of Turkish EFL learners. Depending on this main research problem, the

underlying theoretical concepts behind the thesis; respectively contrastive interlanguage analysis

(CIA), computer learner corpus (CLC) research, and semantic prosody (SP), will be discussed in

detail under this title.

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2.3.1. Contrastive Interlanguage Analysis (CIA)

Linguists have always been dealing with comparing different language systems in pursuit of

eliciting learning difficulties. Contrastive analysis, thus, is considered to have a great contribution

to understanding many dimensions of second or foreign language learning process. On the other

side, the emergence of ‘interlanguage’ theory helps researchers examine learner’s current first

language abilities and their progress towards the target language. Contrastive Interlanguage

Analysis (CIA), which can be considered as an integration of these two approaches, namely

contrastive analysis and interlanguage development research, was proposed by Granger in 1996.

Unlike contrastive analysis ‘in the traditional sense’ embarking on a comparison between the

mother tongue and second language, Contrastive Interlanguage Analysis (CIA) mainly depends on

comparing learner data with native speaker (L2 vs. L1). This new approach, which Selinker (1989)

regarded a “new type of CA” and which Granger called CIA “lies at the heart of CLC-based

studies” (Granger, 1998: 12). According to Granger (1998: 12), “CIA involves two major types of

comparison: (1) NL vs. IL, i.e. comparison of native language and interlanguage, and (2) IL vs. IL,

i.e. comparison of different interlanguages” as shown in the diagram below:

Figure 1: Contrastive Interlanguage Analysis

Source: Granger (1996: 44)

Granger (2015: 8) explains the abbreviations that she used in this diagram with her own

statements as follows:

In this first presentation of CIA, native and learner languages were abbreviated as NL and IL

(interlanguage) respectively and the language used to illustrate the method was English: E1 for

native English and E2 for English as a foreign language. The IL/IL branch of the method was

illustrated with interlanguages representing different mother tongue backgrounds: English of

French learners (E2F), German learners (E2G), Swedish learners (E2S) and Japanese learners

(E2J).

16

Granger (2015: 8) also uses some other abbreviations or ‘labels’ during the presentation of CIA

approach: ‘NS’ represents “native speaker while ‘NNS’ refers to “non-native speaker”. In addition,

‘L1’ is abbreviated for “first/native” and ‘L2’ for “foreign/second language.”

Before in-depth evaluation of Contrastive Interlanguage Analysis, as one of the significant

concepts that the present study adopts by nature, the theory of ‘interlanguage’ needs inevitably a

broad explanation to shed light on the progress that CIA went through until its emergence.

2.3.1.1. What is Interlanguage?

An American linguist Selinker (1972) created the concept ‘interlanguage’ which refers to a

linguistic system. According to Ellis (1997: 33), interlanguage centers on the mother tongue but

again dissimilar with it and the target language. In other words, it is a kind of linguistic state

isolated from both EFL learners’ mother language and the target language. The term interlanguage

was derived from Weinreich’s (1953) “interlingual identifications” (as cited in Selinker 1972: 211).

Today, in addition to interlanguage, the term “language learner language” has been also preferred

by researchers in linguistic studies (Lennon, 2008: 56).

Selinker (1972) describes interlanguage as “a dialect whose rules share characteristics of two

social dialects of languages, whether these languages themselves share rules or not” (Corder, 1981:

17). In other words, the interlanguage is a linguistic organization “in its own right” (Lakshmanan

and Selinker, 2001: 395). It can be supposed to be a stage when the learner is not able to have

fluency and accuracy in language proficiency. Hence, the linguistic proficiency of a learner in

second language or foreign language learning process can precisely be characterized with the

analysis of interlanguage in its specific set of rules. Being discrete in the sense that learner

language is clearly different form mother language and target language, interlanguage is likely to

have the following main features suggested by Trawinski (2005: 54):

erroneous (containing language errors on various language levels)

systematic (rule-governed and common to all learners)

dynamic (constantly changing through the gradual process of accommodation of new rules)

distinct from the L1 and L2 (including grammatical/lexical/phonological elements of both

language)

permeable (open to amendment, not fixed)

17

Figure 2: Interlanguage

Source: adopted from Corder (1981: 17)

The diagram above simply illustrates the theory of interlanguage in which ‘Language A’

stands for first language (L1). In an attempt to explain it further, Larsen-Freeman and Long (1991:

60) point out that interlanguage may be better recognized when it is considered to be a

“continuum” between the mother tongue and the second language. The interlanguage is a system

having its own rules. In its core sense, interlanguage can also be termed as a linguistic system in

progress under the influence of a learner’s both mother tongue and the second language. Likewise,

as Tarone (2006: 747) asserts, the learner language is considered as a detached system in linguistics

which remains diverse from the mother tongue and the target language, but needs both of them to

identify the learners’ interlingual behaviours.

Selinker’s (1972) identification of five principle cognitive processes underlying interlanguage

concept is summarized by Ellis (1994: 351) as follows:

1. Language transfer

2. Transfer of training

3. Strategies of second language learning

4. Strategies of second language communication

5. Overgeneralization of the target language material

These five processes refer to (1) transmitting the knowledge from one language to another (not all

but some elements might be transmitted from mother language to second language), (2)

transmitting the competencies acquired while learning language (a number of interlanguage items

may arise from the aspect of teaching), (3) the techniques used for acquiring foreign language (the

target language learner may define the learning equipment), (4) the techniques for communication

(the target language learner may define the appropriate path for taking part in conversation with

target language user) and (5) employing the general rules for the all elements of foreign language

(the items produced in the process of interlanguage arise from the obvious generalizing of

18

principles and lexical points of target language). This list appears to be valuable in terms of

presenting “mental processes responsible for L2 learning” as well as “key distinctions” such as

overgeneralization and language transfer (Ellis, 1994: 351).

The above-mentioned processes of interlanguage proposed by Selinker are visualized in

following diagram to clarify the mutual relationship among SL, TL and IL:

Figure 3: The Five Mental Processes of Interlanguage

Source: Krezeszowski, 1977 (as cited in Paradowski, 2008)

In his well-known essay “Interlanguage”, Selinker (1972: 214) also introduces three types of IL

produced utterances which are regarded as “psychologically relevant data of L2 learning”: “(1)

utterances in the learner’s native language (L1) produced by the learner; (2) IL utterances produced

by the learner; and (3) TL utterances produced by native speakers of that TL.”

It is crucial to state that interlanguage development plays a vital role in throwing light on the

undiscovered features of second or foreign language learning process. For Hasselgärd and

Johansson (2011: 33),

Learning a foreign language is a slow and, for most people, difficult process which rarely leads

to full mastery. Even advanced language learners make mistakes and normally have a limited

repertoire compared with native speakers of the target language. Problems may be linked to

features of the target language, the learner’s first language or to the learning process itself.

Revealing features of learner language, or interlanguage, has become an important means of

surveying both obvious and more subtle differences between interlanguage and native speaker

performance, and can potentially lead to improved language teaching as well as insights into the

processes of language learning.

In general terms, the concept of interlanguage looks for the answer of two questions (Ellis,

1997: 31) which ask for the nature of linguistic utterances in the target language and how those

utterances evolve as time passes. In order to seek an answer to such inquiries related to

interlanguage development, plenty of linguistic researches can be done through large scale of

computerized corpus analysis using statistical methods. The present contrastive corpus-based study

19

can be shown as an example to interlanguage investigation because it analyzes semantic prosodic

behavior of EFL learners while using English intensifiers in their argumentative essays. The study

is conducted on Turkish EFL learners who are currently going through a developmental process in

language learning. Further insights from previous studies of interlanguage would certainly clarify

the scope of such investigations utilizing learner corpora.

First of all, Hinkel’s (2002) book called Second Language Writers’ Text is actually based on

an interlanguage investigation examining academic written essays of native and non-native

speakers. The purpose of the study is to find out linguistic features of same written tasks given to

both native speakers and advanced non-native learners from different first language backgrounds

such as Chinese, Japanese, Korean, Vietnamese, Indonesian and Arabic nationalities studying in

US universities. The corpus compiled from 1457 essays, 1215 of non-native speakers and 242 of

native speakers, has a size of approximately 434.000 words. The study mainly analyzes the

frequency of 68 different linguistic features and patterns, 26 of which are found to have a

significant difference between L1 and six groups of English Language learners. The statistics show

that ELL texts have higher usage of the forms of ‘to be’ as the main verb, “predicative adjectives,

public and private verbs”, however, writing samples in L1 include “passive voice, perfect aspect,

and reduced adjective clauses”.

Another interlanguage research conducted by Kil (2003) analyzes five immigrant Korean

English learners in U.S. in terms of the common error in their writing samples. The focus was on

common error types such as ‘word order errors, co-occuring articles and determiners’ as well as

‘excessive use of wrong expressions (overgeneralization)’. Kill (2003: 247) concludes that the

errors in learner language progress in time.

In his dissertation, Zhang (2008) investigates the acquisition of ‘to be’ by Chinese learners of

English through a corpus-based approach. By comparing two Chinese learner corpora compiled

from English essays of students from Hong Kong and Mainland China, he aimed at giving a

general overview of the functions of BE in writing. The pedagogical implication drawn from the

research is that it finds solutions to challenges that the learners of second language might face and

presents strategies to overcome the difficulties in obtaining ‘to be’ and its related constructs.

As a recent example to interlanguage research, Asikin (2017) examined the existence and

underlying reasons of interlanguage in EFL students’ narrative writings. Ten narrative texts written

by nine Indonesian students in a high school in city of Kuningan were analyzed concerning the

concept of interlanguage introduced by Selinker in 1972. The results showed that the existence of

interlanguage errors were found in passive sentences, verb selection, using auxiliary verbs, and

translation. According to the researcher, interlanguage occurs due to an effect of first language.

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From pedagogical point of view, it was implied that students should have an adequate exposure to

appropriate use of English grammar.

The current literature from Turkish L1 background abounds with studies of interlanguage

analysis of written discourse (Atasever, 2014; Bozdağ, 2014; Bozdağ and Badem, 2017; Can, 2009;

Eveyik-Aydın, 2015; Kilimci and Can, 2008; Kilimci, 2001; Özbay and Bozkurt, 2017; Şanal,

2007; Taş, 2008). However, corpus-based interlanguage studies in Turkey through the use of

KTUCLE (Karadeniz Technical University Corpus of Learner English) as the local learner corpus

of the study as well as TICLE (Turkish International Corpus of Learner English) which was

introduced by Kilimci and Can (2008) as a subcorpus of ICLE (International International Corpus

of Learner English) deserve initial mention since the present study utilizes both of them as learner

corpora under investigation.

Şanal (2007) made a lexical investigation in the written language of Turkish EFL learners by

contrasting TICLE and LOCNESS, which are also used as research corpora in my study. Through

the use of a computerized contrastive methodology, the researcher was in pursuit of exploring

learners’ lexical complexity and richness, the differences between reference and learner corpus in

terms of frequently used tokens, and stereotypes of learner corpus. The conclusion that Şanal

(2007: 77) drew from his analysis was that the reference corpus has a more complex lexical

diversity and density in comparison to the learner corpus. There were also some evidences of

overuse and underuse patterns of certain lexical items under the linguistic influence of learners’

mother tongue.

Moreover, Bozdağ (2014) investigated the interlanguage behaviours of Turkish EFL learners

in academic written discourse. In this corpus-based study, the researcher compared TICLE with

LOCNESS to find out the lexical preference of EFL learners by focusing on ten frequently used

“AKL (Academic Keyword List) verbs”. According to the results, the learners were found to

overuse and underuse some certain verbs in academic writing when compared with native speakers.

In their comparative interlanguage analysis, Özbay and Bozkurt (2017) examined tertiary

level Turkish EFL students’ expository argumentative essays in terms of the usage of complex

prepositions by comparing two corpora, respectively LOCNESS and KTUCLE, which is also the

local learner corpus in present research. The focus of paper was on overused and underused or

misused prepositions used by the learners in certain contextual situations. The results indicated that

the overuse and underuse profile in learner corpus have differences in comparison to reference

corpus.

A closer look to the prior relevant literature on the theory of interlanguage suggests that the

methodology of contrastive corpus-based analysis of learner corpora gives researchers and linguists

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an opportunity to analyze interlanguage development of language learners besides their linguistic

awareness, lexical preference, and potential in making errors, or many other aspects in the process

of language learning.

2.3.1.2. Historical Overview of Contrastive Interlanguage Analysis

Before the emergence of the idea of ‘interlanguage’; linguistic studies employing

comparative procedures had undergone a significant shift from contrastive analysis (CA) to

contrastive interlanguage analysis. According to contrastive analysts, “the second-language

learner’s language was shaped solely by transfer from the native language” (Tarone, 2006: 747).

That is to say, contrastive analysis suggested that potential errors in foreign language learning

process mainly stem from the differences between L1 and L2. It is believed that by a comparison,

many problems and difficulties that learners may encounter in second language learning can be

predicted. As Mackey (2006: 435) stated, “differences between the learner’s L1 and L2 were

thought to be the main source of difficulty for L2 learners, and the phonology and grammatical

structures of languages were compared to predict areas of difficulty. This became formally known

as the contrastive analysis hypothesis (CAH).”

Contrastive Analysis Hypothesis was introduced by Lado (1957) in his book called

Linguistics Across Cultures which is accepted to have launched the contrastive analysis movement

in language teaching (Tajareh, 2015: 1106). Lado (1957) was the first who suggests comprehensive

procedures for contrastive analysis of languages predicting learner difficulties. In his own remarks,

Lado (1957: 2) asserts that “we assume that the student who comes in contact with a foreign

language will find some features of it quite easy and others extremely difficult. Those elements that

are similar to his native language will be simple for him, and those elements that are different will

be difficult.” Lado (1957: 59) also details this issue as follows:

Since even languages as closely related as German and English differ significantly in the form,

meaning, and distribution of their grammatical structures, and since the learner tends to transfer

the habits of his native language structure to the foreign language, we have here the major

source of difficulty or ease in learning the structure of a foreign language. Those structures that

are similar will be easy to learn because they will be transferred and may function satisfactorily

in the foreign language. Those structures that are different will be difficult because when

transferred they will not function satisfactorily in the foreign language and will therefore have to

be changed.

Although there was a consensus on the idea that comparison of languages could facilitate

predicting errors, later researches have shown that challenges in language learning process cannot

be reliably predicted by differences between languages. For example, Whitman and Jackson (1972)

examined four different contrastive analysis conducted in English and Japanese languages and

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arrived at the conclusion that contrastive analysis may not predict alone the interference problems

of interlanguage (as cited in Larsen-Freeman and Long, 1991: 56).

According to Mackey (2006: 435), “studies have indicated that very few of the errors

produced by L2 learners can be traced to their L1. Instead, many errors are due to developmental

processes common to all learners regardless of L1 background.” Also, CAH was criticized by many

researchers because “many errors predicted by Contrastive Analysis were inexplicably not

observed in learners' language” (Rustipa, 2011: 17). According to Rustipa (2011), Contrastive

Analysis could not estimate difficulties in learning, but it is good at explaining errors.

Moreover, Wardhaugh (1970) distinguishes “the strong and the weak version” of the CAH.

According to the strong version, “contrastive analysis would be able to predict all learning

problems” while weak version implies that “contrastive analysis could explain the cause of many

but far from all, systematic-language learning errors” (as cited in Celce-Murcia et.al, 1996: 20).

Based upon this distinction, Larsen-Freeman and Long (1991: 57) made the following explanation:

“The strong version involved predicting errors in second language learning based upon an a priori

contrastive analysis of the L1 and L2, and as we have seen, the predictions are not always borne

out.” On the contrary, in the weak version researchers focus on learner errors by distinguishing

similarities and differences between two languages.

Error analysis (EA) appeared in the 1960s by Corder and his counterparts to criticize the

opinion that contrastive analysis could predict great majority of errors. Until the late sixties, the

general view held by the behaviorists on the issue of SLA was that the process of learning mostly

depends on acquiring new language habits. Therefore, errors made by learners were supposed to be

predicted by comparing mother tongue and target language. As Corder (1981: 1) emphasized that

“what was overlooked or underestimated were the errors which could not be explained in this

way”. It is an explicit fact that “learners from an error analysis (EA) perspective differs vastly from

the view of learners from the CA perspective.” In contrastive analysis, errors occur as a result of

interference with first language habits which are out of the learners’ control. However, in error

analysis, the learner have an active participation in “processing input, generating hypotheses,

testing them and refining them, all the while determining the ultimate TL level he or she will

attain” (Larsen-Freeman and Long, 1991: 61).

In 1970s, there was a shift from behaviorist methods towards various internal aspects dealing

with language acquisition of learners (Mackey, 2006: 435). Interlanguage theory appeared as a

reaction to the CAH, and it began to lose its importance. “While the status of the IL notion has

been maintained in the field, EA, like CA, fell into disfavor” (Larsen-Freeman and Long, 1991:

61). On the basis of the evidence that CA and EA have “weaknesses and failure”, the benefits of

these two hypotheses should not be underestimated in terms of their influence on SLA research.

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For Zhang (2008: 10), a contrastive analysis between mother language and target language enable

researchers to understand the basis of learner language. On the other side, the researcher considers

error analysis as a simple method for analyzing difficulties in language learning process and fruitful

tool for teachers to identify EFL learners’ common problems. Nevertheless, many scholars turned

their attention towards interlanguage development to search for second language acquisition. It was

widely agreed upon that learners` linguistic systems were somehow different from both their L1

and L2. “Interlanguage is a theoretical construct which underlies the attempts of SLA researchers

to identify the stages of development through which L2 learners pass on their way to L2 (or near

L2) proficiency” (Ellis, 1989: 42).

In order or to better understand the scope of Contrastive Interlanguage Analysis, the

researchers should internalize all the aforementioned historical phases it has passed through so far.

An interlanguage analysis gives information about the nature of acquiring a foreign or second

language and focuses on the progress in learner language instead of predicting errors depending on

their first language. In that sense, this study carries out a contrastive analysis to elicit the

interlanguage problems and tendencies of tertiary level EFL learners in using intensifiers

2.3.1.3. Studies on Contrastive Interlanguage Analysis

As noted earlier, Contrastive Interlanguage Analysis compares native corpora with non-

native corpora to reveal linguistic features of learners based on their authentic speech or writing

samples. “NS/NNS comparisons can highlight a range of features of non-nativeness in learner

writing and speech, i.e. not only errors, but also instances of under and over representation of

words, phrases and structures” (Granger, 2002: 12). Some examples of studies applying the

methodology of CIA are reviewed here so as to outline the importance of learner corpora in SLA

and EFL research settings.

It is worth to begin with a study of Granger, who is well-known as the pioneer researcher of

Contrastive Interlanguage Analysis. Granger and her colleague Altenberg (2001) investigated EFL

learner use of high frequency verbs with a special focus on the usage of the verb ‘make.’ In

particular, the study aimed at finding out the overuse and underuse of these verbs by comparing

authentic learner data with computerized native speaker corpora. There were two groups of EFL

learners from Swedish and French backgrounds. The learner corpus was consisted of samples from

ICLE (International Corpus of Learner English) database including the essays of French and

Swedish learners. On the other hand, LOCNES was used as the control native corpus of the study.

According to results, it is obvious that “EFL learners, even at an advanced proficiency level have

great difficulty with a high frequency verb such as make” (Altenberg and Granger, 2001: 173).

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Granger (2004.132) draws the attention to many researchers adopting CIA such as Ringbom

(1998), Aijmer (2002), and Nesselhauf (2003). For instance, Ringbom (1998) identified the use of

high frequency of lexis having generality such as nouns like ‘people’ and ‘thing’ in EFL learners’

written production by making a comparison of learner corpora with a similar native speaker corpus.

In other CIA based research, Aijmer (2002) detected the use of modals by Swedish, German and

French EFL learners by comparing student essays with essays of native speakers. She found out the

overuse of modals by these three learner groups. It is clearly understood from the research that

learners from different language backgrounds prefer different modals which are correspondingly

similar to their native language usage. Likewise, Nesselhauf (2003) explored the use of

collocations by advanced German EFL students based on the data from German subcorpus of ICLE

in comparison with dictionaries and British National Corpus. The study is resulted in that the

influence of first language on the production of English collocations by German EFL learners was

significantly high.

The methodology of comparing learner and native corpus is also adopted by Leńko-

Szymańska (2004) who investigated the overuse and underuse of demonstratives in detailed. In this

two-way comparison analysis, the researcher used the PELCRA corpus of learner English compiled

from written essays of Polish university students and the British National Corpus (BNC) to identify

native-like use of demonstratives. The results showed that Polish EFL learners need specific

assistance in learning this particular feature of language. As Leńko-Szymańska concluded that even

the tiniest details of interlanguage problems which have not been explored yet can be enlightened

by learner corpus data (as cited in Aston et al., 2004: 4).

A recent research from Turkey conducted by Babanoğlu and Can (2018) employs the

methodology of CIA to analyze the use of adverbial connectors in Turkish EFL learners’

argumentative essays. With this in mind, four corpora were compared in this study: three learner

corpora which were respectively TICLE, SPICLE (Spanish Corpus of Learner English), and

JPICLE (Japanese Corpus of Learner English) besides one native speaker corpus, LOCNESS. The

findings obtained from the contrastive analysis of connector usage indicate that the overuse

connectors are commonly used by many Turkish EFL learners. Babanoğlu and Can (2018: 27)

found in the study that the overuse of adverbial connectors in three EFL learner corpora can be

considered as a general inclination for sharing common interlanguage properties.

Another study carried out by Turkish researchers Akbana and Koşar (2015) examines the

highest-frequency vocabulary in advanced learners and native speakers through contrastive

interlanguage analysis. The data retrieved from two corpora, LOCNESS and TICLE, have been

analyzed on the basis of CIA to investigate the use of the top ten words. The study came to the

conclusion that the overuse and underuse of the words seem to have a common interlanguage use.

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Apart from these, in a research carried out by Özbay and Kabakçı (2016), the support verb

construction use of tertiary level EFL learners was examined through native and non-native

academic and argumentative corpora. The study uses two native and two non-native corpora for the

investigation of SVC use: BAWE (British Academic Written English), KTUCALE (Karadeniz

Technical University Corpus of Academic Learner English), TICLE (Turkish International Corpus

of Learner English) and LOCNESS (The Louvain Corpus of English Essays). Özbay and Kabakçı

(2016: 1462) put forward that tertiary level Turkish EFL learners had a tendency to use fewer and

specific SVCs.

2.3.2. Computer Learner Corpus (CLC) Research

Computer learner corpus research (CLC, hereafter) has been a relatively new method which

“is still in its infancy”; and it uses the methods and tools of corpus linguistics to throw light into

authentic learner language (Granger, 1998: xvi). For Leech (1998, as cited in Granger, 1998: 123),

CLC is “a new research enterprise, a new way of thinking about learner language, which is

challenging some of our most deeply-rooted ideas about learner language.” Granger (2004: 124)

simply defines computer learner corpora as “electronic collections of spoken or written texts

produced by foreign or second language learners.” In order to expand upon the explanation of

CLC, Granger (2002: 7) adopted Sinclair’s (1996) definition below:

Computer learner corpora are electronic collections of authentic FL/SL textual data assembled

according to explicit design criteria for a particular SLA/FLT purpose. They are encoded in a

standardised and homogeneous way and documented as to their origin and provenance.

It is an undeniable fact that computers and software corpus tools play an important role in

fast-changing nature of language learning and teaching. Since 1990s, “the rapid development of

automatic data processing and information technology has opened up new prospects for contrastive

approaches through the potential of large corpora” (Tajareh, 2015: 1107). Until quite recently, the

researchers dealing with SLA or EFL studies needed great time and effort to collect and analyze

even small amounts of learner data. Granger (1998: 3) cited Rundell and Stock’s (1992) statement

to emphasize that computers liberate linguists “from drudgery and empowers [them] to focus their

creative energies on doing what machines cannot do.” Accordingly, Hunston (2002: 3) argues:

Strictly speaking, a corpus by itself can do nothing at all, being nothing other than a store of

used language. Corpus access software, however, can re-arrange that store so that observations

of various kinds can be made. If a corpus represents, very roughly and partially, a speaker’s

experience of language, the access software re-orders that experience so that it can be examined

in ways that are usually impossible. A corpus does not contain new information about language,

but the software offers us a new perspective on the familiar.

On the benefit of electronically encoding of corpus data, Baker (2006: 2) asserts that

“complex calculations can be carried out on large amounts of text, revealing linguistic patterns and

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frequency information that would otherwise take days or months to uncover by hand, and may run

counter to intuition.” For Kennedy (1998: 5), “the analysis of huge bodies of text ‘by hand’ can be

prone to error and is not always exhaustive or easily replicable.” However, today, the progress in

technology enables us to compile larger sizes of learner data on computer and analyze them on

linguistic software programs (Granger: 2002: 7).

Before dwelling on the importance of computer-aided analysis of learner corpora in language

teaching and learning settings, the varieties of English language should be clarified in detail.

Granger (2002) states that learner corpora are centered on the non-native varieties of English and

can be categorized as follows:

Figure 4: Varieties of English

Source: Granger (2002: 9)

ENL refers to English as a native language spoken by people who acquired it as their first

language or mother tongue. The non-native varieties of English, on the other hand, include “EOL

(English as an Official Language), ESL (English as a Foreign Language), and EFL (English as a

Foreign Language)”, and in her own explanation, she defined these varieties as (Granger, 2002: 9):

EOL is a cover term for indigenised or nativised varieties of English, such as Nigerian English

or Indian English. ESL is sometimes referred to as Immigrant ESL: it refers to English acquired

in an English-speaking environment (such as Britain or the US). EFL covers English learned

primarily in a classroom setting in a non-English-speaking country (Belgium, Germany, etc.).

Learner corpora cover the last two non-native varieties: EFL and ESL.

The present study focuses on tertiary level EFL students to find out the semantic prosodic

behavior of their intensifier usage based on two learner corpora and a reference corpus. With this in

mind, the use of learner and reference corpora in second or foreign language learning environment

will henceforth be explained in detail.

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2.3.2.1. The Use of Learner Corpora in EFL and SLA Research

In general terms, Hunston (2000: 15) describes learner corpora as the compilation of texts

like essays which are produced by language learners in order to identify to what extent and in what

respects learners differ from each other and from native speakers. In analyzing language learning

use, Hunston (2002: 212) puts emphasis on two major advantages of learner corpora over other

methods in corpus-based studies:

Firstly, it makes the basis of the assessment entirely explicit: learner language is compared with,

and if necessary measured against, a standard that is clearly identified by the corpus chosen. If

that standard is considered to be inappropriate (if, for example, the appropriate target for

Norwegian school children is considered to be expert Norwegian speakers of English rather than

British speakers of English), then the relevant corpus can be replaced. Secondly, the basis of

assessment is realistic, in that what the learners do is compared with native/expert speakers

actually do rather than what reference books say they do. Many of the parameters of difference

noted, such as vocabulary range, or word-class preference, do not appear in most grammar

books.

Similarly, Reppen (2006) claims that second language learner corpora are used by researchers

to investigate language patterns of EFL learners “rather than relying on information from case

studies and single examples” (as cited in Babanoğlu and Can, 2018: 17). Comparative studies based

on learner corpora make it perfectly possible for teachers and researchers to find out various types

of learner errors and weaknesses in addition to the differences between native and non-native

performance eliciting interlanguage development properties “which were not or hardly accessible

via the previous methods” (Şanal, 2007: 26).

To Leech (1998), learner corpora is “a useful resource for anyone wanting to find out how

people learn languages and how they can be helped to learn them better” (as cited in Kilimci, 2014:

401). In the preface of book called Learner English on Computer, Leech (1998: xvii) also puts

forward:

…[L]earner corpus is a new phenomenon: it enables us to investigate the nonnative speaking

learners’ language (in relation to the native speaker’s) not only from a negative point of view

(what did the learner get wrong) but from a positive one (what did the learner get right?). For the

first time it also allows a systematic and detailed study of the learner’s linguistic behaviour from

the point of view of ‘overuse’ (what linguistic features does the learner use more than a native

speaker?) and ‘underuse’ (what features does the learner use less than a native speaker?)

Finally, authenticity is an important criterion for learner corpora. James (1992) noted “the

really authentic texts for foreign language learning are not those produced by native speakers for

native speakers, but those produced by learners themselves” (as cited in Baker et al., 2006: 103). In

other words, a learner corpus contains L2 output produced by language learners of any proficiency

level. However, the aspect of authenticity is considered somehow problematic with regard to

learner language. For Granger (2002: 8),

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Even the most authentic data from non-native speakers is rarely authentic as native speaker

data, especially in the case of EFL learners, who learn English in the classroom. We all know

that the foreign language teaching context usually involves some degree of ‘artificiality’ and

that learner data is therefore rarely fully natural. A number of learner corpora involve some

degree of control.

Upon authenticity, Widdowson (1998, 2000; as cited in Tono, 2016: 35) argues that corpus

data is not authentic language since the text is separated from its original context, and he also

claims that “in language learning contexts, learners are often authenticate genuine text as they do

not belong to the community for which the texts are created.”

2.3.2.2. Corpus Compilation and Design Criteria

According to Sinclair (1996), “a corpus is a collection of pieces of language that are selected

and ordered according to explicit linguistic criteria in order to be used a sample of the language”

(McEnery et al., 2006: 4). The corpora are built for various purposes “which in turn influence the

design, size and nature of the individual corpus” (Kennedy, 1998: 3). The most frequent corpus

types can be listed as “specialised corpus, general corpus, comparable corpora, parallel corpora,

learner corpus, pedagogic corpus, historical or diachronic corpus, and monitor corpus” (Hunston,

2002: 15-16). This study deals with written learner corpus to compare with native speakers.

Granger (2002: 10) also lists different kinds of learner corpus typology designed or compiled for

many different purposes such as synchronic or diachronic, monolingual or bilingual, general or

technical, spoken or written.

Briefly to say, there are some essential aspects in corpus compilation such as size, content,

and representativeness (Hunston: 2002: 25). The size of a corpus is a fundamental issue in corpus

design. As it is previously noted, the advances in computer technology make it easy and reliable to

store and analyze even larger size of corpora. As Hunston (2002: 25) claims, “the feasible size of a

corpus is not limited so much by the capacity of a computer to store it, as by the speed and

efficiency of the access software.” Researchers may also prefer using smaller size of corpus to

work on to have an access to reliable data. The content of a corpus depends on the scope and

purpose of the research. Representativeness of a corpus is closely associated with the sampling.

Biber (1993: 243) states that “representativeness refers to the extent to which a sample includes the

full range of variability in a population.” According to Leech (1991: 27), “in practical terms a

corpus is ‘representative’ to the extent that findings based on its contents can be generalized to a

larger hypothetical corpus.” However, the representativeness of a corpus may change over time and

it becomes ‘unrepresentative’ if not updated regularly (Hunston: 2002: 30).

It is quite obvious that a learner corpus should be carefully compiled on the basis of some

certain features. The texts that learner corpus include may not be supposed to be called as

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“naturally occurring texts” like texts in native corpora (Nesselhauf, 2005: 40). A clear design

criteria is a must for the compilation of a learner language corpus since it is ‘heterogeneous’ for

holding various kinds of learners from different learning backgrounds (Granger: 1998: 7). Granger

(1998) lists some of the main features which are relevant to learner corpus compilation:

Table 1: Learner corpus design criteria

Language Learner

Medium Age

Genre Sex

Topic Mother Tongue

Technicality Region

Task Setting Other Foreign Languages

Level

Learning Context

Practical Experience

Source: Granger (1998: 8)

Granger (1998: 8) explains that the attributes in the category of language shown in the table

are quite similar to criteria used in native corpus design. Medium represents written or spoken

corpora in which different genres can be used such as “argumentative vs. narrative writing or

spontaneous conversation vs. informal interview.” The topic is of high importance since it has an

effect on ‘lexical choice’ while technicality determines both the “lexis and grammar.” Task setting

is related to features such as preparedness (time limit) or being part of an exam. Except for “age

and sex”, all the attributes belonging to the learner are unique to learner corpora. Learner’s mother

tongue, other foreign languages s/he knows, language varieties spoken in his or her region,

language level, learning context, and practical experience all have a potential to influence ‘L2

output’.

2.3.2.3. Reference corpora

Reference corpus represents the standards and norms of native speakers. It is supposed to be

large enough to comprise all varieties and characteristics of a language in order to be used as a

reliable reference. Leech (1998: xv) defines the reference corpus as “a standard of comparison, a

norm against which to measure the characteristics of the learner corpora.” Granger (2002: 12)

highlights that there are quite many native corpora available today for contrastive analysis to take

as a norm. Baker (2006: 43) concantrates upon the benefits of reference corpora in linguistic

researches. Iniatially, he states that reference corpora can represent a specific genre to provide

evidence of its discourse. He adds that “a reference corpus acts as a good benchmark of what is

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‘normal’ in language, by which your own data can be compared to”. In addition to these, reference

corpora can be used to test linguistic theories (Baker, 2006: 43).

It seems an obvious truth that writing texts produced by native speakers can serve as

professional criteria to be compared for eliciting learners’ interlanguage development. As Adel

(2006; as cited in Paquot, 2010: 72) puts forward,

On the one hand, it can be argued that in order to evaluate foreign learner writing by students

justly, we need to use native-speaker writing that is also produced by students for comparison.

On the other hand, it can also be argued that professional writing represents the norm that

advanced foreign learner writers try to reach and their teachers try to promote. In this respect, a

useful corpus for comparison is one which offers a collection of what Bazerman (1994: 131)

calls ‘expert performances.’

What is more, Hunston (2002: 20) highlights the importance of using native corpora in

language studies as follows:

A corpus essentially tells us what language is like, and the main argument in favor of using a

corpus is that it is a more reliable guide to language use than native speaker intuition is.

Although a native speaker has experience of very much more language than is contained in

even the largest corpus, much of that experience remains hidden from introspection… Intuition

is a poor guide to at least four aspects of language: collocation, frequency, prosody and

phraseology.

Thus, the current study which is mainly based on semantic prosodic analysis of English

intensifiers employs the utilization of native corpus in comparison with learner corpora to

investigate interlanguage development of EFL learners. Through the use of corpora, one can

analyze the prosodic nature of lexical items, namely semantic prosody, which refers to “the

spreading of connotational coloring beyond single word boundaries” (Louw, 1993: 157). When the

learner corpora, KTUCLE and TICLE are compared with a reference corpus such as LOCNESS, it

means that L1 corpus is taken as a standard reference with which the learner data from a L2 corpus

will be compared.

2.3.3. Semantic Prosody (SP)

Semantic prosody has aroused intense interest among corpus linguists for almost twenty years

(Stewart, 2010: 6). “Louw was the linguist who introduced the term semantic prosody to the

linguistic field” (Al-Sofi et al., 2014: 121). He explained the concept as “a consistent aura of

meaning with which a form is imbued by its collocates” (Louw, 1993: 157). This is obviously a

short definition, but the use of metaphors is worth to be explained in detail to understand the SP

concept in a broader sense. Semantic prosody is a meaning transferred from one word to another,

and here this transfer is expressed metaphorically with the verb ‘to imbue’ which can be simply

explained by Whitsitt (2015: 288) as follows:

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…in order to better understand these terms and how they are linked, we need to understand

what the word imbue means and how it is used. In the Oxford English Dictionary we find

“imbue” as a transitive verb with a literal and figurative meaning. The literal sense is linked

with fluids: when one imbues something, it means: “to saturate, wet thoroughly (with moisture);

to dye, to tinge, impregnate (with colour.).” The figurative meaning refers to immaterial

principles, such as spirit, moral vigor, beliefs, as in “to impregnate, permeate, pervade, or

inspire (with opinions, feelings, habits, etc.).” What seems to be happening here is that

immaterial things such as spirit, meanings, and beliefs can be transferred by imbuing because

they are thought of as liquids.

There is another metaphor used in Louw’s well-known definition that meaning is thought of

as an ‘aura’ which is described in Oxford English Dictionary as “the distinctive atmosphere or

quality that seems to surround and be generated by a person, thing, or place.” Similar to Louw’s

above-mentioned metaphorical description of SP as “aura of meaning”, Bublitz (1996) suggests

that “words can have a specific halo or profile, which may be positive, pleasant and good, or else

negative, unpleasant and bad” (as cited in Cheng, 2013: 1).

To define the concept better, Louw (2000: 60) expands the scope of SP in his working

definition:

A semantic prosody refers to a form of meaning which is established through the proximity of a

consistent series of collocates, often characterisable as positive or negative, and whose primary

function is the expression of the attitude of its speaker or writer towards some pragmatic

situation.

It can be clearly derived from above-quoted description that Louw’s understanding of

semantic prosody depends on the consistency of two fundamental issues that are ‘collocation’ and

‘attitudes’. Semantic prosody is often found in the company of the term ‘collocation’. This fact can

be explained briefly as follows “if several different words all sharing the same semantic trait are

frequently used with another word, meaning will be passed, over time, from that group of words to

the other word” (Whitsitt, 2000: 284). In other words, in semantic prosody certain individual words

have a tendency to collocate with positive, negative, or neutral items by nature. According to

Stubbs (1995: 25), “it is becoming increasingly well documented that words may habitually

collocate with other words from a definable semantic set.” Hunston (1999) also agrees that “briefly,

a word may be said to have a particular semantic prosody if it can be shown to co-occur typically

with other words that belong to a particular semantic set” (as cited in Hunston and Francis, 2000:

104).

Another definition is provided by Sinclair (1996), who had originally suggested the idea of

SP to Louw during a “personal communication in 1988” as Louw himself stated in his well-known

article “Irony in the Text or Insincerity in the Writer?” (Louw, 1993: 158). Even though Louw

(1993) is known as the first researcher who introduced the concept of semantic prosody, Sinclair

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initially mentioned and caught the attention to it. As it is deduced from the following definition,

Sinclair (1996: 87) actually adopts a pragmatic approach to SP:

Semantic prosody…is attitudinal, and on the pragmatic side of the semantics / pragmatics

continuum. It is thus capable of a wide range of realisation, because in pragmatic expressions the

normal semantic values of the words are not necessarily relevant. But once noticed among the

variety of expression, it is immediately clear that the semantic prosody has a leading role to play

in the integration of an item with its surroundings.

Partington (2004: 131) states that Sinclair (1987, 1996, 1998), Louw (1993) and Stubbs

(2001) are among the most notable linguistics elaborating on the concept of semantic prosody.

Hunston (2007) puts forward that Sinclair’s study of set in (1991), Stubbs’ study on cause (1995)

and Louw’s analysis of utterly (1993) are the common examples on semantic prosody which have

caught the most attention among many other linguists. In the first place, Louw’s example of adverb

‘utterly’ which carries a negative semantic prosodic effect, is mostly combined with undesirable

collocations such as depression, terrified, against, destroying, demolished, etc. Retrieved from

Cobuild corpus, above-mentioned concordances of ‘utterly’ indicates that it has mostly ‘bad’

prosody and just a few ‘good’ collocations (Louw, 1993: 161). Based on Cobuild corpus, Louw

also finds out that bent on usually collocates with pretty unpleasant items; for instance, bent on

destroying / harrying / mayhem (Partington, 2004: 133).

In addition, Stubbs (1995) explains in the abstract of his article that “using data from corpora

of up to 12 million words, it is shown that the lemma CAUSE occurs in predominantly ‘unpleasant’

collocations, such as cause of the trouble and cause of the death.” Since cause tends to collocate

frequently with negative items, researchers are more likely to label it as bearing negative prosody

(Wachter, 2008: 13). However, this negative prosodic identification of the lemma cause is

criticized by Huntson (2007) by emphasizing the significance of context. According to Huntson

(2007: 263):

With respect to CAUSE, for example, it would be possible to suggest that this verb loses its

association with negative evaluation when it occurs in ‘scientific’ registers. A more sustainable

argument, however, might be that…the attitudinal meaning associated with CAUSE applies only

when the ‘caused entity’ concerns animate beings, their activities and goals. Where the ‘caused

entity’ is an inanimate object unrelated to human goals no attitudinal meaning is implied. If a

register makes more use of the second phenomenon than the first, it will appear that in that

register CAUSE has no attitudinal meaning.

It is clearly understood from this explanation that Hunston agrees with Stubbs who thought cause

generally tends to have an undesirable meaning when the context is related to something animate or

human properties. In the example of Wachter (2012: 13), “these proteins cause a smell to be less

strong”, cause refers to inanimate thing, and it cannot be regarded as having negative prosody. That

is to say, semantic prosody depends on the collocations of a word.

33

Moreover, Sinclair gives an example to semantic prosody by putting an emphasis on the

negative effect of phrasal verb ‘set in’. The collocations of this phrasal verb always has a bad

semantic profile in the data he examined. On set in, Sinclair (1987, as cited in Partington, 2004:

132) notes:

The most striking feature of this phrasal verb is the nature of the subjects. In general they refer

to unpleasant states of affairs. Only three refer to the weather; a few are neutral, such as reaction

and trend. The main vocabulary is rot (3), decay, ill-will, decadence, impoverishment, infection,

prejudice, vicious (circle), rigor mortis, numbness, bitterness, mannerism, anticlimax, anarchy,

disillusion, disillusionment, slump. Not one of these is desirable or attractive.

Partington (1998: 68) explains semantic prosody as “the spreading of connotational coloring

beyond single word boundaries.” Partington (1998: 67) adds that “often a favaourable or

unfavourable connotations is not contained in a single item, but is expressed by that item in

association with others, with its collocates.” Partington (1998: 67) provides an example to

unfavorable prosody with the term rife since it has a tendency to co-occur with words like

“corruption, violence, speculation, crime, misery and disease.”

Concerning all the examples above, Partington (2004) points out that in such cases, the word

seems to collocate frequently with many items sharing distinct semantic set, but having evaluative

meaning in common. Similarly, Hunston and Thompson (2000: 5) emphasize the attitudinal

function of SP which they describe as “the speaker or writer’s attitude or stance towards, viewpoint

or feelings about the entities and propositions that he or she is talking about.” It is also defined as

an indicator of good or bad (Partington, 2004, 131).

It is well-known that computer-held studies have greatly contributed to the semantic prosodic

analysis in recent years. “Semantic prosodies have, in large measure and for thousands of years,

remained hidden from our perception and inaccessible to our intuition” (Louw, 1993: 173). In other

words, human introspection is not a reliable source for retrieving semantic prosodies. In this vein,

Zethsen (2006: 279) highlights the significance of corpus studies:

The important discovery of the existence of semantic prosodies means that we cannot reveal

connotative meaning in a text by simply looking at individual words. We must take into account

the wider semantic/collocational patterns which these words form part of in order to reach the

evaluations which are likely to be triggered in a reader’s mind and for this we need computers

and corpus studies.

Xiao and McEnery (2006: 106) also assert that “yet as the size of corpora has grown, and

tools for extracting semantic prosodies have been developed, semantic prosodies have been

addressed much more frequently by linguists as exemplified” and summarized below by them:

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Table 2: Examples of Semantic Prosody

Author Negative Prosody Positive Prosody

Sinclair (1991)

BREAK OUT

HAPPEN

SET in

Louw (1993, 2000)

bent on

build up of BUILD up a

END up verbing

GET oneself verbed

a receipe for

Stubbs (1995, 1996, 2001a, 2001b)

ACCOST PROVIDE

CAUSE career

FAN the flame

signs of underage

teenager(s)

Partington (1998)

COMMIT

PEDDLE/peddler

Dealings

Hunston (2002) SIT thorough

Schmitt and Carter (2004) bordering on

Source: Xiao and McEnery (2006: 106)

2.3.3.1. Semantic Prosody as an Extended Unit of Meaning (EUM)

“The starting point of the description of meaning in language is the word” writes John

Sinclair (2004: 24) in his book Trust the Text to put an emphasis on the importance of words as the

smallest basic units conveying the meaning. However, Stubbs (2009: 124) puts forward that an

individual word is not enough for conveying meaning. The meaning of an individual lexical item

may differ in a context through its connections with different words. In other words, the unit of

meaning lies at the heart of the phrases that a word collocates with.

One of the forerunners of corpus linguistics, Sinclair’s model of ‘the unit of meaning’ is

consisted of four types of co-selection categories: “collocation, colligation, semantic preference

and semantic prosody.” The following descriptions of these four units of meaning are ordered from

concrete to abstract (Stubbs, 2009: 124-125):

(1) COLLOCATION is the relation of co-occurrence between an obligatory core word or phrase

(the node) and individual COLLOCATES: word/tokens which are directly observable and

countable in texts.

(2) COLLIGATION is the relation of co-occurrence between the node and abstract grammatical

categories (e.g. past participles or quantifiers). A traditional category such as ‘‘negative’’ may

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be realized grammatically (would not budge) or semantically (would hardly budge, refused to

budge).

(3) SEMANTIC PREFERENCE is the relation of co-occurrence between the phrasal unit and

words from characteristic lexical fields. Recurrent collocates provide observable evidence of the

characteristic topic of the surrounding text (e.g. typical subjects or objects of a verb).

(4) SEMANTIC PROSODY is the function of the whole extended unit. It is a generalization

about the communicative purpose of the unit: the reason for choosing it (and is therefore related

to the concept of illocutionary force).

To sum up, the model of ‘Extended Units of Meaning’ has the following main components:

Table 3: The components of EUM

COLLOCATION tokens co-occurring word-forms

COLLIGATION classes co-occurring grammatical classes

SEMANTIC PREFERENCE topics textual coherence

SEMANTIC PROSODY motivation communicative purpose

Source: (Stubbs, 2009: 125)

As Tognini-Bonelli (2001: 19) adds, “the units thus described are ‘extended units of

meaning’ because, having started with a node as a core, they have incorporated other words in the

co-text that appeared to be co-selected with it and form a regular pattern.” Last but not least,

Sinclair (1996, as cited in Zethsen, 2006: 279) concludes that “so strong are the co-occurrence

tendencies of words [collocation], word classes [colligation], meanings [semantic preference] and

attitudes [semantic prosody] that we must widen our horizons and expect the units of meaning to be

much more extensive and varied than is seen in a single word.”

2.3.3.2. Semantic Preference vs. Semantic Prosody

Semantic preference is “the relation, not between individual words, but between a lemma or

word-form and a set of semantically related words” (Stubbs, 2001: 65). In other words, semantic

preference is closely related to the issue of collocation in which a particular lexical item collocates

not with another individual item, but with a series of items in a semantic field (Partington, 2004:

150). For instance, Stubbs examines the word large in a corpus having 200 million tokens and

reveals that the 25% of 56,000 examples frequently collocates with lexical items such as “number,

scale, part, amounts, quantities etc.” belonging to the same semantic category; and the semantic

preference of large is typically “quantities and sizes” (Begagić, 2013: 404). Semantic prosody, on

the other side, is regarded by Sinclair as “an obligatory property of a unit of meaning, although it

may be more or less explicit in any one example” (Hunston, 2007: 250) essentially being either

positive or negative in its context. There are various “two-term” evaluations of SP in the literature:

“positive and negative, favourable and unfavourable, desirable and undesirable” (Morley and

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Partington and, 2009: 141). When a word is not associated with positive or negative items in a

context, it can be considered as having neutral prosody in nature (Ünaldı, 2013: 42).

For Stubbs (2009: 126), “the concepts of semantic preference and semantic prosody has

received substantial commentary” among many linguists such as Partington (2004) and Hunston

(2007). However, these two units of meaning in Sinclair’s EUM model seem to lead to confusion

on many readers. In a similar sense, Bednarek (2008: 119) states that these two terms are likely to

be often confusing due to being two distinct but related phenomena at the same time. According to

Stubbs (2001), “the distinction […] is not entirely clear-cut. It is partly a question of how open-

ended the list of collocates is: it might be possible to list all words in English for quantities and

sizes, but not for unpleasant things” (as cited in Partington, 2004: 149). Hence, Stubbs (2009: 126)

offeres that the complication between semantic preference and semantic prosody might be

prevented by using different terms “in order to distinguish between semantic relations (to the topic

of the surrounding text) and the pragmatic function (of the whole phrasal unit).”

To McEnery and Hardie (2012: 138), “semantic preference links the node to some word in its

context drawn from a particular semantic field, whereas semantic prosody links the node to some

expression of attitude or evaluation which may not be a single word, but may be given in the wider

context.” Sinclair’s analysis of the naked eye can be given as an example to clarify the distinction

between these two concepts. According to the concordances below, Sinclair (as cited in McEnery

and Hardie, 2012, 138) discusses that expression of “the naked eye has a semantic preference of

visibility” which is clearly derived from the words such as “seen, visible and perceived.”

…too faint to be seen with the naked eye

…it is not really visible to the naked eye

…cannot always be perceived by the naked eye

In other respects, the expression of “the naked eye has a semantic prosody of difficulty.” However,

this semantic meaning cannot be deduced from the isolated words in the context, but by pragmatic

interpretation of readers of analysts (McEnery and Hardie, 2012: 138). “Semantic prosodies are

evaluative or attitudinal and are used to express the speaker’s approval (good prosody) or

disapproval (bad prosody) of whatever topic is momentarily the object of discourse” (Sinclair,

(1996, as cited in Partington, 2004: 150).

The core of the current study is on intensifiers which are the attitudinal way of expressing

one’s perspective, opinion, or point of view. Therefore, the main focus of the analysis and

discussion will be on semantic prosody within Sinclair’s EUM model, which is evaluative and

attitudinal in nature.

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2.3.3.3. Studies Related to Semantic Prosody

Recently in the field of corpus linguistics, a great many investigations have been carried out

on semantic prosody by well-known linguists mentioned before (Louw, 1993; Sinclair, 1996;

Partington, 1998, 2004; Stubbs, 2001). In addition to western studies, the SP concept has begun to

arouse attention in the past few years among many scholars from other countries, especially from

China.

To begin with the Chinese researchers, Wei (2002) investigated semantic prosody of the verb

CAUSE in English language, and he found out that it has a negative semantic prosody similar to

other researchers’ findings. His analysis also indicated that there are different semantic prosodic

features in specialized genres and texts. For instance, the word cause has predominantly negative

prosodic nature in academic texts than general writing texts (Zhang, 2009). Wang and Wang (2005)

also conducted a comparative study on the semantic prosodic analysis of the lemma CAUSE. The

study focused on the English written texts produced by native speakers and Chinese learners, and

utilized two corpus: Chinese Learner English Corpus (CLEC) and part of the British National

Corpus (SBNC). With this in mind, the collocates of CAUSE were retrieved from the two corpora

and then two of most frequent collocates were determined for comparison: change(s) and great(er,

est). The analysis indicated that a significant difference occurred between native speakers and non-

native Chinese learners concerning semantic prosodic usage of CAUSE. Chinese EFL learners

were found to underuse the typical negative semantic prosody of the lemma CAUSE while they

tend to overuse its positive SP.

McEnery and Xiao’s (2006) study compared the collocational semantic prosodic behavior of

three groups of near-synonyms in English with those in Chinese language from a cross-linguistic

perspective. They used English and Chinese corpora to conduct their research. Based on the

statistical analysis, they drew the conclusion that semantic prosodic nature of both languages are

observable and similar in manner. Similarly, another investigation carried out by Gong and Wu

(2012) examined the issue of collocation, semantic prosody and near-synonymy like McEnery and

Xiao (2006). Their focus was on the collocational and semantic prosodic nature of two verbs used

for ‘help’ in Mandarin Chinese, and they utilized a Chinese corpus called Chinese Word Sketch to

make their analysis. According to the results of their research, it was found out that the near-

synonymous words have different collocations in terms of semantic prosody.

Zhang (2010) conducted a comparative study on Chinese EFL learners’ semantic prosodic

use of the verb COMMIT by contrasting learner corpus CLEC and reference corpus BROWN. The

analysis indicated that Chinese EFL learners had somehow similar prosodic behaviour when

compared to native speakers. Nonetheless, some collocations and patterns of semantic prosody far

38

from nativeness and harmony were observed in EFL learners who were in their interlanguage

development period.

Sardinha (2000) carried out a contrastive corpus-based investigation identifying semantic

prosody of equivalent items in English and Portuguese languages. According to the findings, the

striking features of semantic prosody were observed through a 140-million word corpus of

Portuguese. BNC is also used as reference corpus. In their corpus-based analysis, Oster and Lawick

(2008) investigated translation aspects of co-occurrence patterns for selected idioms in German,

Spanish and/or Catalan in order to find out similarities and differences regarding semantic

preference and semantic prosody.

McGee (2012) examined the semantic prosodic awareness of three groups of participants

consisted of non-native university undergraduate students, Arab English teachers, and native

speaker English teachers in terms of seven lexical items, respectively bring about, cause,

completely, face, potentially, provide, regime. The goal of the research was to investigate to what

extent two non-native speaker groups of different English language level are aware of the semantic

prosodic usage of these words in comparison to native speakers. The results showed that semantic

prosodic awareness did not actually develop in non-native learners’ use of lexical items under

investigation, whereas the group of native speakers was observed to have semantic prosodic

tendencies for all seven items.

Begagić (2013) researched one of the most frequently used V-N collocations make sense in

terms of semantic preference and semantic prosody, which are two notions that have been recently

studied in corpus linguistics. First 100 occurrences of make sense were selected randomly from the

Corpus of Contemporary American English (COCA) and analyzed manually in detail.

As a Turkish researcher, Ünaldı (2013) examined the semantic prosodic properties of the

word pose in English in COCA, a 464-million-word corpus. The collocational occurrences of the

target word pose in academic contexts were compared with other genres. The findings indicated

that pose seems to combine with negative lexical items in academic contexts, whereas in others it

has a tendency to have neutral semantic prosody.

There are also studies examining the semantic prosodic aspects of Turkish language. For

instance, a corpus-based research implemented by Aksan et al. (2008) aimed at analyzing two

groups of synonymous Turkish words: (1) aşk, sevda, sevgi; standing for ‘love’; and (2) Tanrı,

Allah; for ‘God’. The study utilized the METU Turkish Language Corpus to examine these word

groups in terms of semantic prosody. Çalışkan (2014) also had a study based on different semantic

prosodic aspects of Turkish language. The target node words of the study, namely “aksettirmek,

...dan ibaret, ...nin teki, karşı karşıya, varsa yoksa”, were analyzed through the demo version of the

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Turkish National Corpus. In addition, Kara (2017) carried out another research concerning the

issue of semantic prosody in Turkish language based on Turkish National Corpus. His study

investigated Turkish near-synonymous words “örgüt, kurum, kuruluş, teşkilat, şebeke” all denoting

a meaning of ‘institution’. Pilten (2017) also elaborated on the concept of SP by giving examples

from Turkish language and previous relevant studies from abroad.

2.4. Intensifiers

Many researchers (Quirk, et al., 1985; Partington, 1993; Lorenz, 1999) have extensively

studied intensifiers. Bolinger (1972: 17) defines the term intensifier as “any device that scales a

quality, whether up or down or somewhere between the two.” Intensifiers are also labelled as

‘degree adverbs’ or ‘degree modifiers’. With regard to this issue, Quirk et al. (1985: 445) came up

with an explanation that “most commonly, the modifying adverb is a scaling device called an

intensifier, which cooccurs with a gradable adjective.”

Different forms of intensifiers have been categorized in reference books of English grammar

as well as in earlier studies. Stoffel (1901) is the first linguist who made a distinction between

“intensives and down-toners, which respectively express a high or a low degree of the adjective

that is being premodified, e.g. extremely good and rather good” (Wittouck, 2011: 11). This

categorization was later adopted and developed by Quirk and his counterparts. Referring to

intensifiers as degree adverbs, Quirk et al. (1985: 445) identify two subcategories of intensifiers

which are “amplifiers and downtoners”. Amplifiers indicate “scale upwards from an assumed

norm” (e.g ‘a very funny film’) while downtoners “have a generally lowering effect, usually

scaling downwards from an assumed norm” (e.g ‘It was almost dark’). Bolinger’s (1972) definition

of intensifiers is similar to that of Quirk et al.’s; however, he divided intensifiers “differently into

Boosters (Quirk et al.’s Amplifiers), Compromisers (Quirk et al.’s Approximators and

Comprimisers), Diminishers, and Maximizers” (as cited in King, 2016: 2). Lorenz (2002) also

categorizes intensifiers into “five semantic categories according to their sources: (a) scalar, which

just scale a quality, with no additional propositional content, e.g. very; (b) feature copying, which

achieve ‘their intensifying effect by copying a substantial part of the adjective’s denotation’ as in

blatantly clear; (c) evaluative, which express an evaluation on the part of the speaker, e.g.

ridiculously low; (d) comparative, like eminently or especially; and (e) modal, which state the

degree to which the quality holds true” (as cited in Méndez-Naya, 2003: 378). The table below

demonstrates different categorizations of intensifiers suggested by eminent scholars:

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Table 4: Different Categorizations of Intensifiers

Scholar Categories for Scale of Degree

Stoffel (1901) Intensives, Down-toners

Bolinger (1972) Boosters, Compromizers Diminishers, Minimizers

Quirk et al. (1973) Amplifiers (Maximizers, Boosters)

Downtoners (Approximators, Compromizers, Diminishers, Minimizers)

Biber et al. (1999) Amplifiers / Intensifiers, Diminishers / Downtoners

Lorenz (2002) Scalar, copying, evaluative, comparative, modal

Quirk et al.’s (1985) classification of intensifiers actually deserves special mention since it is

widely recognized and cited in many recent studies related to the use of intensifiers (Tagliamonte,

2008; Wittouck, 2011; Yaoyu and Lei, 2011; 2014; Özbay and Aydemir, 2017; Wang, 2017).

Quirk et al. (1985: 590) classify amplifiers into “two subgroups a) maximizers which can denote

the upper extreme of the scale b) boosters which denote a high degree, a high point on the scale.”

Downtoners, on the other hand, are divided into four subcategories: (a) approximators which refer

“to express an approximation to the force of the verb”, (b) compromisers that “have only a slight

lowering effect”, (c) diminishers that “scale downwards and roughly mean ‘to a small extent”, (d)

minimizers which are “negative maximizers (not) to any extent” (Quirk et al., 1985: 597). The

following table illustrates all subcategories of intensifiers proposed by Quirk and his colleagues:

Figure 5. The Subsets of Intensifiers

Maximizers (e.g. completely)

(I) AMPLIFIERS

Boosters (e.g. very much)

Approximators (e.g. almost)

(II) DOWNTONERS Compromisers (e.g. more or less)

Diminishers (e.g. partly)

Minimizers (e.g. hardly)

Source: Quirk et al., (1985: 589-590)

The subcategory of intensifiers proposed by Quirk et al. (1985) will be henceforth the focus

of the present study, and the research will mostly revolve around amplifiers.

2.4.1. Amplifiers

The current study focuses on amplifiers because they are the most frequent category of

intensifiers in British National Corpus (Kennedy, 2003: 472). According to Altenberg (1991: 128),

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“what makes amplifiers an interesting category to study from a collocational point of view is that

they are subject to a number of syntactic, semantic, lexical and stylistic restrictions affecting their

use in various ways and fostering a great deal of competition between them.” In a similar vein, on

amplifiers, Granger (as cited in Lorenz, 1999: 27) remarks:

They constitute a particularly rich category of lexical collocation involving as they do a

complex interplay of semantic, lexical and stylistic restrictions and covering the whole

collocational spectrum, ranging from restricted collocability – as in bitterly cold – to wide

collocability – as in completely different/new/free etc.

Briefly, the amplifying intensifiers “scale upwards from an assumed norm” (Quirk et al.,

1985: 590). For Biber et al. (1999: 554), “traditionally, degree adverbs that increase intensity are

called amplifiers.” The amplifiers are divided into two categories: “(a) ‘maximizers’ (absolutely,

completely, entirely, totally, utterly, etc.), which denote an absolute degree of intensity and

therefore occupy the extreme upper end of the scale, and (b) ‘boosters’ (very, awfully, terribly,

bloody, tremendously, etc.), which denote a high degree but without reaching the extreme end of

the scale” (Altenberg, 1991: 128). According to Altenberg (1991: 129), maximizers and boosters

differ in terms of “their different demands on the gradability of the intensified element.”

Maximizers signify ‘nonscalar’ items which “do not normally permit grading (e.g. empty,

impossible, wrong)”, whereas boosters basically modify ‘scalar’ items which are “fully gradable

(e.g. very beautiful)”.

Many EFL learners consciously overuse adjective intensification in their written performance

on the purpose of keeping readers’ attention and focus. Another reason for non-native learners’

tendency to overuse of amplifiers is that they would like to conceal their insufficient knowledge of

vocabulary. “The learners not only use more intensification, they also use it in places where it is

semantically incompatible, communicatively unnecessary or syntactically undesirable” (Lorenz,

1998: 64). EFL learners believe that an amplifier may be easily replaced by its synonyms in a

context. Although amplifiers can be regarded as near-synonymous pairs and can be used

interchangeably in the same pattern, there are slight differences related to their meaning. Near-

synonymous lexical items that have similar meanings pose a challenge for EFL learners.

2.4.2. Studies on the use of Intensifiers by EFL Learners

There are a great number of studies investigating EFL learners’ use of intensifiers in spoken

and written language. As being relevant to the scope of present study, the researches elaborating on

intensifier use especially in writing are widely mentioned in this section. A couple of investigations

belonging to prominent scholars or linguists (Lorenz, 1998; Granger, 1998; Kennedy, 2003;

Partington, 2004) are initially presented. Then, various studies conducted by other researchers, who

42

are studying or interested in applied linguistics, are discussed to throw light at the issue of EFL

learners’ intensification in their writing.

First of all, Lorenz (1998) examined adjective intensification in German EFL students’

writing by comparing them with British native speaker students. Totally four corpora were used in

the study: two learner corpora compiled from German teenagers and German university students of

English, and two native speaker corpora, namely ICLE and LOCNESS. The findings show that

advanced German EFL learners were observed to overuse adjective intensification.

Furthermore, Granger (1998) compared French EFL learners with native speakers of English

concerning their use of –ly ending amplifiers in writing. Only three amplifiers (completely, totally

and highly) were found to have statistically significant differences. French students overused

completely and totally while they underused highly. On the other hand, the frequency of boosters

was less than the ones native speakers used. It is found out that French EFL learners had a tendency

to use fewer amplifier collocations than native speakers. Besides, French learners had a tendency to

use proper collocations that might be resulted from the L1 transfer (Granger, 1998: 6).

Kennedy (2003) thoroughly analyzed the use of twenty four most frequently used amplifiers

in British National Corpus. The researcher examined frequencies and collocations of selected

amplifiers. The maximizers under investigation were “fully, completely, entirely, absolutely, totally,

perfectly, utterly, and dead”. The selected boosters were “very, particularly, clearly, highly, very

much, extremely, badly, heavily, deeply, greatly, considerably, severely, terribly, enormously, and

incredibly”. Fundamentally, this particular research is crucial because the seven of the selected

maximizers, excluding dead, are also analyzed in this thesis.

As the title of his paper “Utterly Content in Each Other’s Company” suggests, Partington

(2004) examined a group of maximizers used in British English by utilizing 10 million-word

Cobuild corpus. The amplifiers “absolutely, perfectly, entirely, completely, thoroughly, totally and

utterly” were analyzed in terms of semantic preference as well as semantic prosody so as to find

out whether they collocate with favorable or unfavorable items. The table below summarizes the

observations that Partington (2014) made throughout his study on intensifier usage:

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Table 5: Partington’s Study on Intensifiers

Maximizer Preference for Prosody

absolutely hyperbole, superlatives

perfectly favourable

utterly absence / change of state unfavourable

totally absence / change of state

completely absence / change of state

entirely absence / change of state, (in) dependency

thoroughly emotions / liquid penetration

Source: Partington (2014: 218)

Liang (2004) compared Chinese EFL learners’ way of maximizer use with native English

speakers in their spoken language. The frequency of maximizers in Chinese corpus was found to be

less than those used by native English speakers. The researcher made the inference based on the

finding that non-native English speakers are supposed to have an insufficient knowledge of

maximizers; thus they are likely to use the same maximizers with different collocates in various

contexts.

Yaoyu and Lei (2011) investigated amplifiers in the doctoral dissertations of Chinese EFL

learners to evaluate their use in academic writings in a strict sense. There were two corpora in the

study compiled by the researchers. CND is the corpus consisted of 20 dissertations written by

native Chinese EFL learners, while USD is the control corpus consisting of the dissertations by

native English speakers in the United States of America. The results indicated that Chinese EFL

learners seem to use more amplifier collocations when compared to native speakers.

Many investigations on intensifier collocation or adjective intensification in second or foreign

language learning concern corpus studies of semantic prosody. For example, Eriksson (2013)

investigated American and Swedish journalists’ use of maximizers in writing. With this purpose,

maximizers are analyzed in two corpora which are SWENC (The Swedish-English Corpus) and

TIME (Time Corpus of American English). Based on the investigation, the researcher concluded

that there are some differences between Swedes and Americans in terms of maximizer usage. The

frequency of each maximizer between the two corpora does not show a significant difference.

However, larger variations were found in the use of intensifier collocations and semantic prosody.

Turkish researchers Özbay and Aydemir (2017) examined tertiary level Turkish EFL

learners’ use of intensifiers based on two learner corpora named as Karadeniz Technical University

Corpus of Academic Learner English (KTUCALE) and British Academic Written English

(BAWE). The study attempted to investigate EFL learners’ semantic prosodic awareness while

using maximizers such as “absolutely, completely, entirely, totally, utterly, fully and perfectly” in

44

their academic writing. The results indicated that Turkish EFL learners are observed to have a

restricted knowledge of intensifiers. According to the findings, absolutely, entirely, fully and

perfectly are found principally positive, while completely is observed as a neutral maximizer. On

the other hand, totally and utterly have negative semantic prosody with their high proportion of

negative collocates.

The subject of intensifiers and their semantic prosodic nature have recently attracted

much interest from Chinese researchers, as well. Zhang (2013) conducted a study on semantic

prosodic change of intensifiers based on historical and modern corpus data. The Bank of English

Corpus (BOE) and the Corpus of Late Modern English Texts (CLMETEV) were utilized in the

study. Four adverbial intensifiers were examined in the study, namely “terribly, awfully, horribly

and dreadfully”. The results showed that intensifiers having negative semantic prosody may co-

occur with items in neutral or positive meaning.

Huang (2007) conducted a research on Chinese EFL learners in terms of their semantic

prosodic behavior in adjective intensification based on two corpora: CLEC and BNC. According to

results, the semantic prosodic use of amplifiers was generally in parallel with that of native

speakers, but the frequency, types, and idiomaticity of amplifier collocations varied when language

proficiency of the learners have a progress in level. In addition, Chinese learners were observed to

misuse totally and very much in terms of semantic prosody and they have a tendency to underuse

the positive semantic prosody of terribly.

Kim and Lee (2009) analyzed synonymous lexical items such as totally, absolutely, utterly,

completely, and entirely by comparing contemporary authentic American language and Korean

English textbook use. The American National Corpus consisting of 3.9 million spoken and 18.5

million written words was exploited for reference to make the analysis of authentic data. The

Korean High School textbooks were composed of 291,501 words. A closer look at results revealed

that intensifiers absolutely and entirely have semantic prosody, with a frequency percentage that is

more than half the total frequency rate. On the other hand, the words totally, utterly and completely

have an orientation towards negative semantic prosody.

Furthermore, Dao (2014) examined English amplifiers absolutely, completely, and totally

from grammatical and functional aspects by using the Corpus of Contemporary American English

(COCA) and various mini-corpora (MC) of everyday language use. These mini-corpora contain

677,930 words compiled from authentic texts such as letters of complaint, letters of application,

chart descriptions, fairy tales, event announcements, bad news deliveries, and everyday

conversations. The conclusion drawn from the statistical analysis showed that absolutely can

probably co-occur more with positive adjectives, verbs, and adverbs; whereas the amplifiers

completely and totally tend to modify items that have a negative semantic meaning.

45

Yang (2014) investigated Chinese English Learners’ usage of one of the downtoners

somewhat by comparing them with native speakers in terms of features such as semantic prosody,

semantic preference, frequency, collocation, and colligation. The native English corpus used in the

present study is the BYU-BNC developed in accompany with the BNC. The learner corpus of the

study is the Chinese Learners’ English Corpus (CLEC). The Chinese EFL learners tend to use less

somewhat when compared with the native English speakers. As Yang (2014: 2336) stated that the

downtoner usage of Chine EFL learners is significantly different in terms of collocations,

colligation, semantic preference and semantic prosody.

Su (2016) conducted a study on intensifiers used by second or foreign language learners of

English in China. Four general intensifiers were selected for the focus of the research: quite, pretty,

rather, and fairly. There were three corpora in the study: the British National Corpus (BNC), the

Written English Corpus of Chinese Learner (WECCL), and the Chinese Learner English Corpus

(CLEC), which are compared in terms of practical usage and semantic prosodic features. It was

observed that native speakers and EFL learners had different preferences and understanding on the

use of four intensifiers in their writing.

Last but not least, the book of Wang (2017) under the title of Patterns and Meanings of

Intensifiers in Chinese Learner Corpora is vital in respect of its scope as the present thesis focuses

on similar issues to investigate on Turkish EFL learners’ intensifier use. The book includes an

extensive corpus investigation of Chinese EFL learners’ amplifier usage distribution and patterns as

well as features related to semantic prosody and preference. The frequency of intensifiers in learner

corpora was in comparison with that in LOCNESS as reference corpus. As major findings, Wang

(2017: 124) found that data distribution of intensifiers in the learner corpus had remarkably

different features from those in the native corpus. A general tendency prevailing in the research

was that Chinese learners of English tend to overuse intensifiers in terms of tokens but underuse

intensifiers in terms of types. Moreover, learners demonstrate specific semantic prosodic behaviors

and semantic preferences in using intensifiers (Wang, 2017: 125).

46

CHAPTER THREE

3. METHODOLOGY

3.1. Introduction

This chapter outlines the methodological framework of the study including procedures for

data selection, collection and analysis. The methodology section also presents design criteria of two

learner corpora and features of one reference corpus utilized in the study.

The aim of the current corpus-based study is to investigate the frequency level as well as

semantic prosodic awareness of tertiary level Turkish EFL students in terms of using English

intensifiers in their expository argumentative essays. By comparing two computerized learner

corpora with a native speaker corpus, this research is conducted to reveal different patterns of

intensifiers and the existing problems of intensifier use in learner English within interlanguage

development period. From a pedagogical point of view, it can be assumed that the evaluation of

intensifier usage by EFL learners focusing on their semantic prosodic awareness offers insights into

the present levels of language learning and teaching settings in Turkey as well as the tertiary level

Turkish EFL learners’ existing practices and preferences in terms of using lexical and grammatical

properties of English with a focus to their usage patterns of intensifiers. In other words, the most

frequently used intensifiers by Turkish EFL learners will be described and presented in tables and

graphics in terms of their patterning and the existing semantic prosodic features.

3.2. Methodological Framework

Methodologically, the study follows quantitative research techniques depending on the

computer-aided analysis of three written corpora under investigation. As explained in the

introductory chapter before, the present study mainly adopts the methodology of Granger’s

Contrastive Interlanguage Analysis (CIA) by nature with the help of two computerized non-native

corpora and one native corpus as illustrated in Table 6:

47

Table 6: The CIA Model of the Study

L2 vs. L1 L2 vs. L2

KTUCLE vs. LOCNESS KTUCLE vs. TICLE

TICLE vs. LOCNESS

One of the above-mentioned non-native corpora, KTUCLE is the local learner corpus of the

study and stands for Karadeniz Technical University Corpus of Learner English. Being an in-

house learner corpus compiled by MA thesis supervisor of the researcher in Karadeniz Technical

University in Turkey, KTUCLE is particularly preferred as the central L2 corpus to examine the

written productions of EFL students in an attempt to study intensifier usage patterns. The second

non-native corpus under research is TICLE (Turkish International Corpus of Learner English) as

Turkish sub-corpus of ICLE (International Corpus of Learner English). The utilization of TICLE

for complementary means helps to reveal alternative data for usage frequency and patterns in

different Turkish speaking EFL settings. All the findings derived from these two leaner corpora

were compared with a native corpus called LOCNESS in order to make a comparison between the

usage patterns and overuse/underuse levels in all corpora. LOCNESS (The Louvain Corpus of

Native English Essays) as the reference corpus employed in this investigation is supposed to shed

light on the standard use of English intensifiers by native speakers.

The learner corpus KTUCLE contains essays written by tertiary level preparatory students of

a Turkish university in the city of Trabzon and all the essays are expository in character. The

second learner corpus TICLE is consisted of essays collected from students in three universities in

Turkey: Çukurova University, Mersin University and Mustafa Kemal University (Kilimci and Can,

2008). The reference corpus LOCNESS contains expository argumentative essays written by

American and British university students (Granger, 1998). It is a corpus of native English made up

of total 361,054 words. The profile and distribution of these corpora are shown in the following

table:

Table 7: The Profiles of the Three Corpora Utilized in the Research

KTUCLE TICLE LOCNESS

Tokens 709,748 223,449 361,054

Texts 1600 280 282+

L1 Turkish Turkish American English, British English

Genre Expository Expository Argumentative Expository Argumentative

As emphasized in the previous sections, this research initially attempts to find out the overall

usage frequency of intensifiers in learner English. Intensifiers are modifying phrases which carry

the traces of speakers’ attitudes. The data distribution of intensifiers in learner corpora in

comparison with those in native corpus will also uncover different usage patterns and features of

48

semantic meaning. While analyzing form or patterns, one can also obtain information about

meaning. Obviously, such descriptions of meaning can be made possible through the analysis of

large-scale corpora.

In the light of corpus data, “the co-occurrence of particular words with particular grammatical

patterns” can be identified (Carter, 2004: 72). According to Hunston et al. (1997) “there are two

main points about patterns to be made: Firstly, that all words can be described in terms of patterns;

secondly, that words which share patterns, share meanings” (as cited in Carter, 2004: 73). In order

to investigate the function of meaning, semantic prosodic attitude of both non-native learners and

native speakers of English in terms of using intensifiers in written production will be evaluated in

this study in a descriptive manner based on Stubbs’ (1996) classification of semantic prosody as

positive, negative, and neutral.

3.2.1. Labelling of Patterns

For pattern analysis of intensifiers, the study employs the approach of ‘pattern grammar’ held

by Hunston and Francis (2000) in their book with the same name. To define it simply, the notion of

pattern can be supposed as a grammatical form of a lexical item. In this study, ‘pattern’ is used as

an indicator showing both the grammar and meaning function of a specific word. The patterns of

major word-classes are simply labeled by Francis et al. (1996, 1998) as follows (Hunston and

Francis, 2000: 51):

v: verb group

n: noun group

adj: adjective group

adv: adverb group

that: clause introduced by that (realised or not)

-ing: clause introduced by an ‘-ing’ form

to-inf: clause introduced by a to-infinitive form

wh: clause introduced by a wh-word (including how)

with quote: used with direct speech

The labeling of intensifier patterns as Wang (2017) employed in his research based on the

method of Hunston’s pattern grammar are adopted in this study. Intensifier plus adjective

combination is labelled by Wang (2017) as ‘INT-adj’ in which intensifer is “the node word that is

capitalized and abbreviated for pattern expression” (Wang, 2017: 52). INT-adj (intensifier +

adjective) is the focused type of investigated intensifier collocations; therefore, other intensifier

patterns such as modifying nouns, adverbs or verbs are not included within the scope of the current

investigation.

49

3.3. The Selection Criteria of Intensifiers

Intensifier is used as an umbrella term to analyze adjective intensification. Quirk et al.’s

(1985) taxonomy of intensifiers is taken as a reference in this study as it is widely-used in related

corpus-based investigations. In this aforementioned categorization, there are two main sub-

categories: amplifiers and downtoners. As their name suggests, amplifiers are the intensifiers that

scale upwards while downtoners are the ones that scale downwards. Prior studies (Lorenz, 1999,

Wang, 2017) have shown that amplifiers outnumber downtoners in learner English. Therefore,

amplifiers were the main focus of the study to see to what degree native or learner speakers use

them in their written production. There are two sub-sets of amplifiers which are maximizers and

boosters. The most frequently used amplifiers are determined and investigated in terms of their

usage patterns and semantic prosodic function. The selected intensifiers under investigation are

shown below:

Table 8: Selected Intensifiers

Amplifiers

Maximizers Boosters

absolutely very

completely so

entirely too

fully

perfectly

totally

utterly

3.4. Corpora and Tools for Data Collection

This corpus-based investigation, as mentioned above, is conducted through the utilization of

three corpora. The first two are Turkish learner corpora and the third one is a reference native

corpus. This section summarizes the design criteria of the learner corpora as well as the features of

native speaker corpus. Besides, corpus tools to be used in the study are presented below.

3.4.1. Learner Corpora

“A type of corpus that is immediately related to the language classroom is a learner corpus”

(McEnery et. al, 2006: 65). It is inevitable that a learner corpus should be carefully compiled and

have a strict design criteria. The design criteria of learner corpora is composed of language and

learner variables. Language variables are categorized into five main subtitles: “medium of

language, genre, topic, technicality and task setting.” Learner variables, on the other hand, identify

features such as “age, sex, mother tongue, region, other foreign languages, level, learning context

50

and practical experience” (Granger, 1985). The following table illustrates the language and learner

variables of KTUCLE as the local learner corpus:

Table 9: The Design Criteria of KTUCLE

Language Variables Learner Variables

Medium written Mother Tongue Turkish

Genre argumentative Age 18-22

Topic education, literature,

society Gender

Female 79%

Male 21%

Technicality academic essay Language Proficiency B2

Task Setting untimed essay 90%

exam 10% Learning Context EFL classroom setting

The table below presents the design criteria of second learner corpus TICLE which is the

Turkish sub-corpus of ICLE (International Corpus of Learner English):

Table 10: The Design Criteria of TICLE

Language Variables Learner Variables

Medium written Mother Tongue Turkish

Genre argumentative Age 21-23

Topic education, society Gender Female 81%

Male 19%

Technicality academic essay Language Proficiency B2-C2

Task Setting untimed essay Learning Context EFL classroom setting

Source: Can, 2011 (as cited in Akbana and Koşar, 2015)

3.4.2. Reference Corpus

In a comparative corpus-based research, the use reference corpus provides the standards and

norms of native speakers. A native control corpus is required to make a comparison between

learner language use and native use (Altenberg and Granger, 2001: 175). The present study utilizes

Louvain Corpus of Native English Essays (LOCNESS) as the control corpus of this research. Table

11, which is constituted based on the information on University of Louvain webpage, illustrates the

features and data distribution of LOCNESS in detail:

51

Table 11: The Profile of LOCNESS

LOCNESS Setting Task Genre Text Number

British Essays

unknown exam literary 15

unknown exam expository – historical 18

unknown exam literary 24

unknown not rigidly timed argumentative 33

British Essays (A Level) unknown untimed essays argumentative Unknown

American Argumentative Essays

Marquette University untimed essays argumentative 46

Indiana University timed essays argumentative 28

Presbyterian College untimed essays argumentative 6

University of South

Carolina

untimed essays argumentative 6

timed essays argumentative 17

untimed essays argumentative 13

untimed essays argumentative 17

University of Michigan timed essays argumentative 43

American Literary Mixed-Essays Presbyterian College exam literary-mixed 16

Total Text Number 282+

Source: https://uclouvain.be/en/research-institutes/ilc/cecl/locness.html

The selection of a reference corpus seems to be a significant methodological decision since it

may affect the outcome of the analysis. It should be better to choose a control corpus having

representativeness and similarities in size, participants, topic, and text types. For this purpose,

LOCNESS is selected as the reference corpus and it is highly comparable to learner corpora for

containing texts written in argumentation by students and having similar age of participants.

3.4.3. Corpus Tools

In corpus linguistics, it may be easy to make an introspection through smaller size of corpora.

However, larger corpora could not be easily analyzed without the help of concordance programs or

computer-aided methods comparing data and estimating their frequency. “A concordance brings

together utterances which have been produced at different times by different speakers, makes

visible recurrent patterns, and allows us to count them” (Stubbs, 2009: 117). Sketch Engine, which

was utilized for the quantitative analysis of the current corpus research, is one of the most preferred

kind of such software tools in corpus linguistics field. Granger (2008: 93) states that by utilizing

such tools, researchers immediately retrieve frequency information of words from their corpus.

This online concordancer contains ready to use corpora; besides, it also enables researchers to

build or upload their own corpora. The three corpora under investigation were uploaded to the

system and the requested data were retrieved automatically for the analysis. All the concordance

52

lines related to intensifier collocations were extracted. Then, the raw frequencies of the data were

normalized per one million automatically by using Sketch Engine program since raw frequencies

do not show the proportional data in comparison process.

3.5. Data Analysis Procedures

The study is mainly based on quantitative research method because it tries to obtain

frequency-based statistical data. In the matter of quantitative analysis of corpus, Stubbs (2009: 117)

stated that “corpora are just data and quantitative methods are just methods, but their combination

has led to a major shift in theory, and it is this theory which has to be evaluated.”

In this study, the frequency of intensifier collocations gathered from two learner corpora and

one native speaker corpus are compared with each other via log-likelihood measurement. LL (log-

likelihood) ratio is regarded as “the most useful statistical device to measure the comparison

between two corpora as it calculates the frequency regarding the corpus word size” (Babanoğlu and

Can, 2018: 21) An online log-likelihood calculator of Lancaster University was used to find LL

scores of selected intensifiers to see whether the frequency differences of three corpora in

comparison have reached any statistical significance. In addition, the rate of overuse and underuse

of intensifiers can be found out with the help of log likelihood measurement. To explain it better, if

the log likelihood score is greater than 6.63, the difference between the two corpora is less than 1%.

The result is usually expressed as p < 0.01 and it means that we are 99% certain of the result. If the

log likelihood score is 3.84 or more, the probability of its happening by chance is less than 5% and

it is expressed as p < 0.05. This means that we are 95% certain of the result. In short, the higher the

critical value, the more significant the difference happens to be between two corpora.

53

CHAPTER FOUR

4. FINDINGS AND DISCUSSION

4.1. Introduction

The present descriptive corpus-based research investigates the use of intensifiers by tertiary

level Turkish EFL learners and native speakers of English with a special focus to their usage

patterns and evaluative meaning. Initially, this chapter reveals the overall data distribution of

intensifiers in native speaker corpus LOCNESS and two Turkish learner corpora, namely KTUCLE

and TICLE. The target intensifiers under investigation are amplifiers which are divided into

subcategories as maximizers and boosters. The selected amplifiers with their adjective collocates

(INT-adj) in three corpora are retrieved and described in terms of their raw and normalized

frequencies with the help of a corpus tool called Sketch Engine. Then, the tendency of tertiary level

EFL learners to overuse and underuse amplifiers in writing are also identified based upon the Log-

likelihood measurements. Finally, the most frequent amplifier collocations of adjectives in learner

corpora and their semantic prosodic profiles as positive, negative or neutral are presented to shed

light on the awareness of EFL learners’ adjective intensification by comparing them with native

speakers of English. The amplifiers in each corpora are also analyzed in terms of their common

usage patterns.

4.2. Overall Frequency Distribution of Amplifiers

In this quantitative research, the overall frequency of intensifiers in each corpus was initially

calculated to make inferences about their usages by native speakers of English and non-native EFL

learners. The raw frequency of each intensifier was normalized into a value per million

automatically by Sketch Engine in order to compare frequencies between corpora of different sizes.

Then, Log-Likelihood (LL) scores were measured in an attempt to illustrate the differences or

similarities between native speakers and EFL learners in overusing or underusing amplifiers in

their written production. Aforementioned in the section of methodology, the LL score greater than

6.63 signifies that the difference between the two corpora is less than 1%. When the LL score is

3.84 or more, the difference between the two corpora is less than 5%. Based upon these

calculations, maximizers and boosters in all corpora are separately analyzed concerning their

overall distribution.

54

4.2.1. Maximizers

As a subcategory of amplifiers, there are seven target maximizers in the study: absolutely,

completely, entirely, fully, perfectly, totally and utterly. The findings on concordance program

reveal that there are 495 additional occurrences of intensifiers modifying adverbs, verbs or nouns in

three corpora under investigation. As the focus of the study, the number of maximizer plus

adjective combinations is 164 in total and they compose 33% of the whole types of occurrences.

Although there are many other occurrences of maximizers in all corpora, the total number of

maximizer plus adjective combinations is limited. Table 12 illustrates the raw frequencies and

percentages of maximizers in an alphabetical order in the three corpora.

Table 12: Overall Distribution of Maximizer + Adjectives

Maximizers

(INT-adj)

LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

Frequency Percentage Frequency Percentage Frequency Percentage

absolutely 5 8,06 221 29,33 3 11,11

completely 15 24,19 282 37,33 11 40,74

entirely 8 12,90 5 6,66 1 3,70

fully 7 11,29 2 2,66 6 22,22

perfectly 14 22,58 2 2,66 0 0

totally 12 19,35 15 20 6 22,22

utterly 1 1,61 1 1.33 0 0

TOTAL 62 100 75 100 27 100

Table 12 shows that in native corpus LOCNESS, completely (f = 15) and perfectly (f = 14)

are the most frequent maximizers while utterly (f = 1) is the least frequent one. On the other hand,

according to the distribution in the local learner corpus KTUCLE, completely (f = 28) and

absolutely (f = 22) have the highest percentages while utterly (f = 1), fully (f = 2) and perfectly (f =

2) have the lowest percentages. Finally, in TICLE, as the second learner corpus of the study,

completely (f = 11) has the highest percentage, whereas perfectly and utterly have no hits, and

entirely (f = 1) is low in percentage.

Considering the overall distribution of maximizers, completely is the most frequently used

maximizer both in native corpus and learner corpora. On the contrary, utterly is the least frequent

maximizer used in the three corpora. Another commonly employed maximizer in KTUCLE and

TICLE is totally which has a function scaling upwards like the maximizer completely. For Kennedy

1 In KTUCLE, there are actually 24 concordance lines of absolutely retrieved from Sketch Engine, but two occurrences

are excluded from the scope of the analysis since they do not collocate with adjectives (see Appendix 2, lines 11-12). 2 Two concordance lines of completely + adjective combinations in KTUCLE are repeated in Sketch Engine, hence the

total number of occurrences is taken as 28. (See Appendix 2, lines 5-10 and 12-13).

55

(2007: 154-155) some amplifiers such as completely and totally are supposed to be synonymous,

but according to British National Corpus (BNC) “even apparently synonymous amplifiers seem to

prefer to keep different company.”

The following tables present the 1R adjective collocations of completely and totally occurred

in both native and non-native corpora. As shown in Table 13 and Table 14, these two distinct

maximizers have a different distribution of collocations. The only common adjective combination

of completely placed in all corpus is ‘completely different’ and obviously it is not the most frequent

collocation in any corpus (KTUCLE f = 5, LOCNESS f = 2, and TICLE f =1). When the number of

tokens in three groups of corpora is taken into consideration, no judgments can be made about the

presence of any overuse or underuse regarding the use of ‘completely different’ as an intensified

adjective collocation.

Table 13: 1R + Adjective Collocations of completely

COMPLETELY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

completely

+

1R adj

indifferent

innocent

different

recyclable

erroneous

unjustified

abhorrent

false

ethical

equal

new

impossible

2

2

2

1

1

1

1

1

1

1

1

1

wrong

different

good

useful

clear

misguided

coherent

independent

innocent

valid

helpful

true

dependent

possible

6

5

4

3

1

1

1

1

1

1

1

1

1

1

unpleasant

opposite

special

safe

adequate

right

theoretical

human

true

equal

different

1

1

1

1

1

1

1

1

1

1

1

TOTAL 15 28 11

As regards totally, there is no common occurrence of intensified adjective collocation in three

corpora. ‘Wrong’ is the most frequent INT-adj collocation in KTUCLE, but TICLE holds only one

occurrence. ‘Different’ and ‘true’ are among the adjectives that collocate no more than once or

twice with the maximizer totally. Surprisingly, there is no occurrence of ‘totally different’ in

KTUCLE unlike to ‘completely different’.

56

Table 14: 1R + Adjective Collocations of totally

TOTALLY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

totally

+

1R adj

unacceptable

dependent

alien

absurd

powerless

abhorrent

blameless

futile

unrealistic

different

2

2

1

1

1

1

1

1

1

1

wrong

bad

useful

distribute

dependable

poisonous

opposite

right

harmful

true

4

2

2

1

1

1

1

1

1

1

different

invaluable

wrong

true

little

2

1

1

1

1

TOTAL 12 15 6

The second highest collocation found in KTUCLE is absolutely. However, the other two

corpora do not display its usage as much as KTUCLE (TICLE f = 3, LOCNESS f = 5). Table 15

represents that there are various adjectives modified by the maximizer absolutely, but no common

collocation has been identified in each group. Similarly as in totally and completely, ‘wrong’ is the

most frequent adjective collocation of absolutely in KTUCLE. The native speakers, on the other

side, prefer using ‘wrong’ just one time in combination with absolutely. It is because the EFL

learners may tend to use the same adjective with near-synonymous maximizers regardless of their

semantic nuances.

Table 15: 1R + Adjective Collocations of absolutely

ABSOLUTELY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

absolutely

+

1R adj

unacceptable

huge

ridiculous

necessary

wrong

1

1

1

1

1

wrong

necessary

right

efficacious

barbaric

express

false

unnecessary

essential

aware

important

9

3

2

1

1

1

1

1

1

1

1

meaningless 1

compulsory 1

impossible 1

1

1

1

TOTAL 5 22 3

As presented in Table 16, perfectly is the second most frequent maximizer in reference

corpus, but it is found to be less favored by EFL learners (LOCNESS f = 14, KTUCLE f = 2,

TICLE f = 0). Even, no occurrence of perfectly plus adjective has been found out in TICLE. The

57

sole common collocation in LOCNESS and KTUCLE is ‘perfectly healthy’. The low usage

frequency of perfectly in learner corpora confirms that Turkish EFL learners often make use of a

small repertoire of maximizers such as completely and totally in writing to enhance the evaluative

value of their focus.

Table 16: 1R + Adjective Collocations of perfectly

PERFECTLY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

perfectly

+

1R adj

legal

natural

safe

good

comparable

understandable

visible

healthy

logical

acceptable

2

2

2

2

1

1

1

1

1

1

healthy

safe

1

1

TOTAL 14 2 0

As shown in Table 17 and Table 18, the maximizers entirely and fully apparently have quite

similar frequencies in LOCNESS. In KTUCLE, both maximizers in adjective head are not much

preferred. LOCNESS and KTUCLE share ‘entirely dependent’ as the only common collocation. In

TICLE, there is only one occurrence of entirely.

Table 17: 1R + Adjective Collocations of entirely

ENTIRELY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

Entirely

+

1R adj

true

separate

unfounded

voluntary

contradictory

dependent

ethical

2

1

1

1

1

1

1

obsolete

man-made

clear

wrong

dependent

1

1

1

1

1

unnecessary

1

TOTAL 8 5 1

Whereas the two maximizers entirely and fully may both appear to be roughly synonymous,

they each collocate strongly with different adjectives as illustrated in Table 17 and Table 18.

58

Table 18: 1R + Adjective Collocations of fully

FULLY LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

fully

+

1R adj

integrated

presidential

reassured

intergrated3

redundant

human

aware

1

1

1

1

1

1

1

useless

individual

1

1

conscious

human

functioning

3

2

1

TOTAL 7 2 6

The expression of ‘fully human’ is the only common collocation that appear in LOCNESS

and TICLE. At first sight, one can suppose that ‘human’ is a noun, so it should be beyond the scope

of the study. On the contrary, the concordances extracted from both corpora confirm that they have

an adjective function (e.g. the unborn is not fully human). According to online Oxford Learner’s

Dictionary, ‘human’ in adjective form means “having the same feelings and emotions as most

ordinary people (e.g. He's really very human when you get to know him.)”. When the proficiency

level of EFL learners get higher, they can be able to distinguish between the correct word classes in

English.

According to Table 19, the last and the least frequent maximizer is utterly which has no

common adjective collocations in each corpora. It is a rarely used maximizer either in combination

with adjectives or other items.

Table 19: 1R + Adjective Collocations of utterly

UTTERLY

LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

utterly

+ 1R adj

devoid 1

different 1

TOTAL 1 1 0

It can be concluded that Turkish EFL learners significantly overuse a limited range of

intensifiers such as completely, totally and absolutely. However, some certain maximizers such as

perfectly are rarely used or not preferred by EFL learners when compared to native speakers of

English. This issue may be a characteristic feature of interlanguage during which EFL learners’

linguistic ability does not match with that of native speakers in language production. The learners

of English may not be competent enough to master vocabulary in an efficient way.

3 The mispelling of the adjective ‘integrated’ is corrected and accounted as having two occurences in LOCNESS.

59

4.2.2. Boosters

The second amplifier category investigated in this research is boosters which are commonly

used in both spoken and written language in English. There are three target boosters: so, too, and

very. The total number of boosters in all corpora is 2926 and they outnumbered maximizers to a

great extent. The boosters are nearly 18 times more than maximizers (f = 164) in the research.

There are 568 boosters in LOCNESS. On the other hand, the learner corpus KTUCLE has 1751 and

TICLE contains 607 boosters in total. The following table presents the distribution of boosters in

native corpus and two non-native corpora in an alphabetical order:

Table 20: Overall Distribution of Booster + Adjectives

Boosters

(INT-adj)

LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

Frequency Percentage Frequency Percentage Frequency Percentage

so 133 23,41 392 22,38 166 27,34

too 97 17,07 343 19,58 77 12,68

very 338 59,50 1016 58,02 364 59,96

TOTAL 568 100 1751 100 607 100

In order to gain a clearer view of typical booster collocation patterns and their usage

distribution, top ten most frequent common boosters in native and non-native corpora were selected

and their LL scores were measured. Then, native corpus was taken as a reference to be compared

with each non-native corpus to reveal learners’ general tendencies to overuse or underuse the

boosters. All the results are presented in separate tables for each booster.

Table 21: Log-likelihood Ratio of very in LOCNESS and KTUCLE

VERY – LOCNESS vs KTUCLE Log Likelihood Scores

LOCNESS

361,054

KTUCLE

709,748

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

important 25 69,20 245 345,19 89,28 +

few 15 41,55 7 9,86 10,85 -

strong 12 33,24 6 8,45 8,11 -

low 6 16,62 2 2,81 5,69 -

hard 6 16,62 25 35,22 3,15

little 11 30,47 10 14,08 3,08

popular 7 19,39 7 9,86 1,57

good 14 38,78 33 46,49 0,33

difficult 12 33,24 23 32,40 0,01

expensive 6 16,62 12 16,90 0

60

To start with very, which has the highest frequency among three boosters with the raw

frequency of 1718 in total, it can be argued that it is highly exploited by the participants in three

corpora with similar usage proportions. When the reference corpus LOCNESS and the local learner

corpus KTUCLE are compared in terms of their overuse and underuse patterns, it can be possibly

inferred that non-native corpus participants overused the intensification ‘very important’ with the

LL score of +89,28, and it is the most frequently used adjective in KTUCLE. On the other hand,

LOCNESS participants used the aforementioned intensification in their written materials with

69,20 normalized frequency while KTUCLE participants involved it with 89,28 normalized

frequency. Concerning their normalized frequencies and corpus sizes, the difference between

native and non-native corpus seems significantly high as it leads to the overuse by KTUCLE

students with the LL score of +89,28.

Having mentioned one occurrence of overuse in KTUCLE, there remain three underuse

examples of adjective intensifications: ‘very few’, ‘very strong’ and ‘very low’. As illustrated in

Table 21, LOCNESS holds 15 evidences with the normalized frequency of 41,55, whereas the non-

native corpus participants use ‘few’ 7 times with the normalized frequency of 9,86. Despite being

featured as the largest corpus in the study, KTUCLE has a relatively low frequency of ‘few’; and it

seems that this booster intensification is underused with the LL measure of -10,85. Other recurrent

adjective collocations ‘very strong’ and ‘very low’ are also underused by the EFL learners with

their LL measures of -8,11 and -5,69 respectively. ‘Strong’ holds the normalized frequency of

33,24 in LOCNESS whereas it is included in KTUCLE with the normalized frequency of 8,45.

Table 22: Log-likelihood Ratio of very in LOCNESS and TICLE

VERY – LOCNESS vs TICLE Log Likelihood Scores

LOCNESS

361,054

TICLE

223,449

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

important 25 69,20 44 196,91 18,35 +

few 15 41,55 1 4,47 8,89 -

strong 12 33,24 1 4,47 6,43 -

difficult 12 33,24 17 76,08 4,92 +

hard 6 16,62 11 49,22 4,86 +

little 11 30,47 15 67,12 4,02 +

good 14 38,78 16 71,60 2,80

expensive 6 16,62 2 8,95 0,63

popular 7 19,39 3 13,42 0,30

low 6 16,62 4 17,90 0,01

61

Very + adjective comprises four overuse and two underuse examples in TICLE when

compared to LOCNESS. TICLE participants overuse the adjective intensifications ‘very

important’, ‘very difficult’, ‘very hard’ and ‘very little’ with the LL measures of +18,35, +4,92,

+4,86 and +4,02 correspondingly. The booster + adjective collocation ‘very important’ is the

highest occurrence of overuse since LOCNESS has a normalized value of 69,20, while TICLE has

196,91. Thus, the LL score of +18, 35 makes this collocation the most overused one as occurred in

KTUCLE. Besides, ‘very difficult’ (LL = +4,86), ‘very hard’ (LL = +4,92) and ‘very little’ (LL =

+4,02) are three other overused collocations with approximately similar LL scores.

In addition to the overuses in TICLE, there are two underuse examples of very + adjective

collocations: ‘very few’ and ‘very strong’. Correspondingly, the two non-native learner groups are

found to show a tendency to underuse ‘very few’ and ‘very strong’ in writing. However, ‘very low’

which is the third underused intensifier collocation in KTUCLE is not an evidence of underuse in

TICLE because its LL score is not found significantly different from the native corpus.

So is the second highest booster intensification having totally 690 occurrences in all corpora

as shown above in Table 20 (LOCNESS f =132, KTUCLE f =392 and TICLE f =166). The

following two tables give the information about the common adjective use with the head of booster

so in all groups of corpora.

Table 23: Log-likelihood Ratio of so in LOCNESS and KTUCLE

SO – LOCNESS vs KTUCLE Log Likelihood Scores

LOCNESS

361,054

KTUCLE

709,748

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

important 2 5,53 44 61,99 24,09 +

little 5 13,84 1 1,40 6,29 -

bad 1 2,76 8 11,27 2,48

many 40 110,78 57 80,31 2,38

useful 1 2,76 7 9,86 1,90

easy 2 5,53 8 11,27 0,92

different 1 2,76 5 7,04 0,88

hard 7 19,38 9 12,68 0,69

much 19 52,62 46 64,81 0,60

difficult 2 5,53 7 9,86 0,57

KTUCLE includes one example of overuse and underuse in so + adjective collocations. ‘So

important’ is applied 2 times in the native corpus while KTUCLE consists 44 raw frequencies of it.

Therefore, this booster + adjective collocation is regarded as being overused in KTUCLE with the

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LL score of +24,09. ‘So little’ has only 1 raw frequency with a normalized frequency of 1,40 in

KTUCLE while LOCNESS involves 5 raw frequencies with a normalized frequency of 13,84.

Thus, this refers to an evidence of underuse with the LL measure of -6,29. ‘So many’ and ‘so

much’ are among the most favored collocations, but the difference between two corpora is not

statistically significant to mention about any instance of overuse or underuse.

While KTUCLE includes one example of overuse of so + adjective collocations, TICLE, by

contrast, involves 3 evidences: ‘so important’, ‘so many’ and ‘so easy’. The adjective

intensification of ‘so important’ holds a normalized frequency of 5,53 in the native corpus while it

exists in TICLE with a normalized frequency of 67,12. Therefore, the LL score calculated as

+18,46 refers to the overuse of ‘so important’ in TICLE as shown in Table 24. ‘So many’ is the

second example of overuse with the LL score of +9,58 in TICLE. The last overused so + adjective

collocation is ‘so easy’ with the normalized frequency of 110,78 in LOCNESS while it holds the

normalized frequency of 214,81 in TICLE. Apart from these, KTUCLE and TICLE hold one so +

adjective collocation in common as an evidence of overuse which is ‘so important’.

Table 24: Log-likelihood Ratio of so in LOCNESS and TICLE

SO – LOCNESS vs TICLE Log Likelihood Scores

LOCNESS

361,054

TICLE

223,449

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

important 2 5,53 15 67,12 18,46 +

many 40 110,78 48 214,81 9,58 +

easy 2 5,53 9 40,27 8,80 +

different 1 2,76 4 17,90 3,65

much 19 52,62 21 93,98 3,34

little 5 13,84 1 4,47 1,33

bad 1 2,76 2 8,95 0,99

hard 7 19,38 6 26,85 0,34

useful 1 2,76 1 4,47 0,11

difficult 2 5,53 1 4,47 0,03

Following the analysis of the adjectives with the booster so, Table 25 and Table 26 introduce

the most common ten booster + adjective collocations with too in KTUCLE and TICLE and their

frequencies in comparison with the native control corpus LOCNESS. Too is the least frequent

booster among three corpora with the total raw frequency of 517 (LOCNESS f= 97, KTUCLE f=

343 and TICLE f= 77) as illustrated before in Table 20 presenting the overall distribution of

boosters.

63

Table 25: Log-likelihood Ratio of too in LOCNESS and KTUCLE

TOO – LOCNESS vs KTUCLE Log Likelihood Scores

LOCNESS

361,054

KTUCLE

709,748

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

much 23 63,70 182 256,42 55,76 +

high 4 11,07 1 1,40 4,52 -

late 5 13,84 23 32,40 3,51

difficult 1 2,76 7 9,86 1,90

important 3 8,30 10 14,08 0,70

long 2 5,53 3 4,22 0,09

many 14 38,77 29 40,85 0,03

big 1 2,76 2 2,81 0

young 1 2,76 2 2,81 0

strong 1 2,76 2 2,81 0

KTUCLE includes the adjective intensification ‘too much’ with a normalized value of 256,42

whereas the native corpus holds a normalized frequency of 63,70. Therefore, the LL score of

+55,76 indicates that it is the sole instance of overused example in KTUCLE. Surprisingly,

KTUCLE, a corpus of 709,748 tokens, has one evidence of underuse concerning too + adjective

collocation, which is ‘too high’ with 1 raw frequency. However, this particular collocation appears

4 times in LOCNESS, and the Log-likelihood score is found -4,52. The remaining adjectives do not

have any statistically significant difference in terms of overuse or underuse of booster too.

One outstanding finding is the use of ‘too many’ that is the second most preferred booster +

adjectives in KTUCLE and LOCNESS. Its normalized frequencies in native and non-native

corpora indicate that there is not a meaningful difference concerning its usage Although ‘too much’

is overly used by non-native English major students when compared to native speakers, the

occurrence of ‘too many’ in KTUCLE is found to be consistent with the native usage frequency.

There is no evidence of underuse or overuse regarding ‘too many’. Another striking result is that

‘much’ is one of the high frequently used adjectives in combination with too, but it does not appear

among the top ten common booster + 1R adjective collocations of very. That is to say, ‘very much’

(LOCNESS f = 2, KTUCLE, f = 11, TICLE f = 4, see Apendix 4,5 and 6) is not preferred as widely

as ‘too much’ in writing. Their semantic difference will be further analyzed in depth in the section

of semantic prosodic description.

The following table displays the normalized frequencies and LL scores of too with the use of

adjectives both in TICLE and LOCNESS:

64

Table 26: Log-likelihood Ratio of too in LOCNESS and TICLE

TOO – LOCNESS vs TICLE Log Likelihood Scores

LOCNESS

361,054

TICLE

223,449

1R adj Raw

Frequency

Normalized

Frequency

Raw

Frequency

Normalized

Frequency LL score

Overuse

Underuse

difficult 1 2,76 6 26,85 6,76 +

much 23 63,70 29 129,78 6,54 +

young 1 2,76 4 17,90 3,65

high 4 11,07 1 4,47 0,77

late 5 13,84 5 22,37 0,57

important 3 8,30 1 4,47 0,31

long 2 5,53 2 8,95 0,23

many 14 38,77 7 31,32 0,22

big 1 2,76 1 4,47 0,11

strong 1 2,76 1 4,47 0,11

While KTUCLE includes one example of overuse and one evidence of underuse when

compared to LOCNESS, TICLE holds no evidences of underuse in terms of too + adjective

collocations. Instead, there exist two overuses of booster + adjectives that are ‘too difficult’ and

‘too much’. ‘Too much’ is the only collocation that is overused in KTUCLE; in other words, it is

correspondingly overused in both non-native corpora.

Moreover, TICLE participants use ‘too difficult’ with a normalized frequency of 26,85 while

the native corpus includes 1 occurrence of it with a normalized frequency of 2,76. Therefore, this

booster + adjective collocation has the LL score of +6,76 which stands for its overuse in TICLE.

Additionally, ‘too much’ is used with the normalized frequency of 63,70 in LOCNESS while it is

employed with the normalized frequency of 129,78 in TICLE. The LL measure of the

aforementioned adjective collocation is +6,54 in TICLE and therefore it is the second example of

overuse in written production .

To sum up, although these three distinct boosters, so, too and very are near-synonymous,

native speakers or EFL learners may intentionally prefer using certain adjectives in the company of

these boosters. Their semantic prosodic profiles described in the following section may hold a

mirror to the EFL learners’ specific preferences for adjective intensification in writing.

4.3. Semantic Prosodic Description of Amplifiers

The language learners should have an awareness of semantic prosodic properties associated

with the usage of intensifiers in a similar manner that native speakers do. This section is concerned

65

specifically with the semantic prosodic analysis of amplifiers in English to reveal similarities and

differences between tertiary level Turkish EFL learners and native speakers in the use of adjective

intensification. Retrieved from both native corpus LOCNESS and non-native corpora KTUCLE

and TICLE, two amplifier clusters, which are maximizers (absolutely, completely, entirely, fully,

perfectly, totally, utterly) and boosters (so, too, very), are analyzed in terms of semantic prosody.

Considering this, all the maximizers and boosters in combination with adjectives are categorized

based on Stubbs’ (1996) classification of SP such as positive, negative, or neutral.

4.3.1. Semantic Profiles of Maximizers

The maximizers under scrutiny are all separately analyzed with a focus on their common

adjective collocates. Initially, the raw frequencies of each maximizer + adjective collocations that

are categorized according to their semantic profiles are shown in Table 27. The typical usage of

maximizers in native corpus is taken as a reference to distinguish improper usages of EFL leaners

and their semantic prosodic behavior.

Table 27: Semantic Prosodic Profiles of maximizers + adjectives

absolutely completely entirely fully perfectly totally utterly

LOCNESS

Positive 2 6 3 3 13 1 0

Negative 3 5 3 1 0 10 1

Neutral 0 4 2 2 1 1 0

KTUCLE

Positive 10 15 1 0 2 4 0

Negative 12 5 3 1 0 11 1

Neutral 0 8 1 1 0 0 0

TICLE

Positive 0 7 0 6 0 2 0

Negative 3 2 1 0 0 2 0

Neutral 0 2 0 0 0 2 0

Depending on the maximizer + 1R adjective collocations in the reference corpus, it can be

inferred that perfectly, completely and fully seem to have positive semantic profile and totally,

absolutely and utterly have a negative profile, while entirely tends to occur between negative and

positive polarity. It must be noted that a certain maximizer may collocate with adjectives bearing a

positive meaning, but in its context it may have an underlying negative evaluation. Thus,

concordance lines in reference corpus should be examined in depth to uncover semantic prosodic

features of each maximizer because the raw frequencies of their adjective collocations may not

reflect their exact semantic orientation in native usage.

Acting mainly as an adjective modifier, absolutely denotes the meaning of “to the fullest

extent; in the highest or utmost degree” (Lorenz, 1999: 83). It is clearly seen in Table 28 that

66

absolutely can be used either with positive or negative adjectives for not being employed in a

regular prosody. Partington (2004: 146) found out that there is “a balance between favourable and

unfavourable items” that collocate with absolutely. The randomly selected examples from online

Oxford Dictionary also indicate positive, negative and neutral prosodic usage of absolutely are

“absolutely incapable, absolutely correct, and absolutely personal.”

Table 28: The Semantic Prosodic Profile of absolutely

ABSOLUTELY

LOCNESS

Positive (2) huge, necessary

Neutral

Negative (3) unacceptable, ridiculous, wrong

KTUCLE

Positive (10) necessary (3), right (2), efficacious, express, essential, aware, important

Neutral

Negative (12) wrong (9), barbaric, false, unnecessary

TICLE

Positive

Neutral

Negative (3) meaningless, compulsory, impossible

The findings related to absolutely in the reference corpus LOCNESS confirms that the use of

this maximizer is distributed in a roughly balanced manner (negative f= 3, positive f=2). The same

situation is valid for the frequency of maximizers in KTUCLE (positive f = 10, negative f= 12).

Such a distribution of adjective collocation in native and non-native corpora may not be enough to

label absolutely as having a positive or a negative prosody, but having a mixed semantic profile to

put it in a simple terms.

Completely is a maximizer which tends to co-occur predominantly with negative adjectives

(Louw, 1993; Paradis, 1997; Kennedy, 2003; Wang, 2017). For Greenbaum (1970, as cited in

Johansson and Stenström, 1991: 137), completely is commonly used with “verbs denoting a failure

to attain a desirable goal or state” (e.g. ‘forget’ and ‘ignore’). In his study based on Cobuild

Corpus, Partington (2004: 148) found out that completely collocates with a number of items

expressing a state of ‘change’ (e.g. “hopeless, ignored, lost, and unexpected”) or ‘absence’ (e.g.

“altered, changed, destroyed, and different”). Oxford Dictionary gives examples in parallel with

Parrington’s findings such as “completely unsatisfactory, completely ridiculous, completely untrue,

completely different, and completely transformed.”

67

Table 29: The Semantic Prosodic Profile of completely

COMPLETELY

LOCNESS

Positive (6) innocent (2), recyclable, ethical, equal, new

Neutral (4) indifferent (2), different (2)

Negative (5) erroneous, unjustified, abhorrent, false, impossible

KTUCLE

Positive (15) good (4), useful (3), clear (1), coherent, independent, innocent, valid, helpful, true, possible

Neutral (5) different (5)

Negative (8) wrong(6), misguided, dependent

TICLE

Positive (7) special, safe, adequate, right, human, true, equal

Neutral (2) theoretical, different

Negative (2) unpleasant, opposite

In contrary to the findings of above-mentioned researchers, completely is observed to have

seemingly a good prosody according to Table 29. Surprisingly, the native corpus in the study also

seems to collocate with adjectives being positive in meaning, but actually many of them have a

negative association in the context as shown in the concordance lines retrieved from LOCNESS:

…a lot for the better, not everything is completely equal. Men are just lime woman, some of them

may (LOCNESS)

… of universal guilt because no one can be completely innocent. Camus raises the idea that even

Jesus (LOCNESS)

KTUCLE which is composed of participants of Turkish EFL learners include a great many

positive adjective collocations with the maximizer completely (positive f = 16). In TICLE, there

appears positive adjectives more than other two prosodic profiles (positive f = 7). It can be inferred

that EFL learners are not fully aware of the negative attitudinal meaning of completely. The results

also point out that the adjectives are mostly compounded with negative prefixes similar to Wang’s

(2007: 90) findings that “the intensifier completely has a strong tendency to collocate with

adjectives with negative prefixes” (e.g. indifferent, unjustified, impossible, abhorrent, misguided,

unpleasant). The only common adjective employed in three corpora is ‘different’ which has no

evidence of prosody for being neutral in meaning.

As shown in Table 30, there is a balanced distribution of negative and positive adjectives

intensified by the maximizer entirely, and the number of neutral items are found to be roughly

similar in LOCNESS. That is to say, there appears no noticeable semantic prosodic distribution of

adjectives in control corpus. In the corpus-based research of amplifiers conducted by Kennedy

68

(2003: 476), entirely is found with items having either positive or negative associations. For

Partington (2004: 148), “the collocations of entirely, however, also seem to encompass a slightly

wider range of senses than those of the others. They include a number of words which express an

opposition between dependence-independence or relatedness-unrelatedness.” It can be claimed that

there is “no clear-cut distinction” for entirely in terms of semantic prosodic categories (Özbay and

Aydemir, 2017: 47).

Table 30: The Semantic Prosodic Profile of entirely

ENTIRELY

LOCNESS

Positive (3) true (2), voluntary

Neutral (2) separate, ethical

Negative (3) unfounded, contradictory, dependent

KTUCLE

Positive (1) clear

Neutral (1) man-made

Negative (3) obsolete, wrong, dependent

TICLE

Positive

Neutral

Negative (1) unnecessary

In present study, the native speakers are likely to use entirely with negative adjectives such as

‘unfounded’, ‘dependent’ or in negative pattern. The two positive intensification examples in

LOCNESS employed with the adjective ‘true’ are used in negation (e.g. ‘not entirely true’). The

result that can be derived from the findings is that entirely collocates mainly with neutral or

negative items. On the other hand, Turkish EFL learners do not highly prefer making use of this

maximizer in their written discourse.

The maximizer fully can be supposed to have a positive orientation. According to Altenberg

1991: 137) synonymous maximizers such as entirely, completely, totally and fully are considered to

“share the sense in every respect.” The definition of fully in Oxford Dictionary is “completely or

entirely; to the fullest extent” and the examples of fully in combination with adjectives include

“fully determined, fully aware, fully candid, fully interactive” all having positive meaning.

Additionally, Kennedy (2003) reported that the maximizer fully has exclusively bond with positive

adjectives having –able or –ible suffix.

69

Table 31: The Semantic Prosodic Profile of fully

FULLY

LOCNESS

Positive (3) reassured, human, aware

Neutral (3) integrated (2), presidential

Negative (1) redundant

KTUCLE

Positive

Neutral (1) individual

Negative (1) useless

TICLE

Positive (6) conscious (3), human (2), functioning

Neutral

Negative

In this study, the only negative prosody in LOCNESS is ‘redundant’ and the sentence is

structured in negation as given below:

…the case, then the human brain will never become fully redundant. There has (LOCNESS)

There are solely evidences of positive associations in TICLE (positive f = 6), whereas no

positive semantic usage is found in KTUCLE. Fully is not a highly preferred amplifier among EFL

learners, though. Instead of it, EFL learners may have a tendency to use completely that can be

more familiar to them in reading materials or books in English language.

Perfectly, as its name suggests, strongly has a positive semantic prosody. According to

Bäcklund (1970) “Perfectly tends to collocate with words referring to positive or commendable

qualities” (as cited in Altenberg, 1991: 137). Partington (2004: 146) stated that perfectly

demonstrates a “distinct tendency” to bond with good things. It can be noted that the adjectives

intensified by this maximizer should not be combined with negative prefixes since perfectly sounds

‘strange’ when collocate with words having negative morphemes such as ‘perfectly unhealthy’

(Paradis, 1997: 81). Likewise, there are13 positive occurrences in LOCNESS as Table 32 displays:

70

Table 32: The Semantic Prosodic Profile of perfectly

PERFECTLY

LOCNESS

Positive (13) legal (2), natural (2), safe (2), good (2), understandable, visible, healthy, logical, acceptable

Neutral (1) comparable

Negative

KTUCLE

Positive (2) healthy, safe

Neutral

Negative

TICLE

Positive

Neutral

Negative

An interesting finding is that despite of being a large corpus KTUCLE has only 2 occurrences

of positive prosody while TICLE has no evidence of any semantic category. In KTUCLE and

LOCNESS, ‘safe’ and ‘healthy’ are common adjective collocations with perfectly. Although

perfectly has a predominant positive reflection in use and creates no prosodic complexity, the EFL

learners do not prefer using it in their writing.

Paradis (1997: 82) stated that “completely and totally are the modifiers par preference with

adjectives with negative morphemes”. Kennedy (2003) also ascertained in his research that totally

co-occurs mainly with negative associations.

Table 33: The Semantic Prosodic Profile of totally

TOTALLY

LOCNESS

Positive (1) blameless

Neutral (1) different

Negative (10) unacceptable (2), dependent (2), alien, absurd, powerless, abhorrent, futile, unrealistic

KTUCLE

Positive (4) useful (2), right, true

Neutral

Negative (11) wrong (4), bad (2), distribute, dependable, poisonous, opposite, harmful

TICLE

Positive (2) invaluable, true

Neutral (2) different (2)

Negative (2) wrong, little

71

It is obviously seen in Table 33 that the negative associations of totally in LOCNESS and

KTUCLE outnumber positive and neutral prosodies. However, TICLE has an equal distribution of

semantic categories. The sole example concerning the positive prosody of totally + adjective

collocation retrieved from LOCNESS is used in negation.

…the scientists involved, by they by no means totally blameless. I suggest the alternative opinion

(LOCNESS)

The particular example below indicates that the EFL learner participants in TICLE may have

no semantic awareness in the use of totally with adjectives.

…from the problems of the life maybe this is not totally true, but not totally wrong. And again

what (TICLE)

Additionally, totally is often used by EFL learners in a positive manner regardless of its

underlying negative prosodic effect.

…and healthy way to use animals on testing. It is totally true that animal testing is beneficial for

(KTUCLE)

…on the other hand, the others say it is totally useful for us. In my opinion cell phone is really

(KTUCLE)

Lastly, the maximizer utterly seems to be associated with unpleasant events. According to the

observations of Greenbaum (1970) and Louw (1993), utterly falls into the category of unfavorable

semantic prosody (as cited in Partington, 2004: 147).

Table 34: The Semantic Prosodic Profile of utterly

UTTERLY

LOCNESS

Positive

Neutral

Negative (1) devoid

KTUCLE

Positive

Neutral (1) different

Negative

TICLE

Positive

Neutral

Negative

72

According to this analysis, utterly is the least frequent type of maximizer + adjective

collocation of all. It has only two evidences, one of which is negative prosody in LOCNESS and

the second one is neutral prosody in TICLE. Thus, the findings cannot be adequate to make any

inference on its semantic prosodic use in learner corpora. It may be concluded that utterly is not

preferred by tertiary level EFL learners in academic writing.

Overall, it can be noted that the target maximizers in the study all have a distinct semantic

prosodic profile. The findings of Bublitz (1998: 26) go in parallel with the results of the current

study: Completely and entirely exhibit a shared “up-scaling” meaning, but concerning the

distribution of negative and positive semantic prosody, these two near-synonymous amplifiers are

not regarded as ‘complementary’. That is to say, they differ in a way that “completely has a clear

negative semantic prosody” while “entirely has no definite semantic prosody at all” which has a

potential to collocate with negative, positive, and neutral items. On the other hand, he added that

the remaining scalar maximizers utterly (negative), totally (negative) and perfectly (positive) have

an obvious semantic prosody.

4.3.2. Semantic Profiles of Boosters

The three selected boosters so, too and very are classified according to their semantic profiles

as shown in Table 35. The boosters can be interchangeably used in combination with adjectives for

expressing negative, positive and neutral evaluations. It is much difficult to identify precise

prosodic nature of boosters due to having a wide range of semantic relationship when compared to

maximizers. All the adjective collocations used with target boosters are given in the Appendices

between 4 and 12.

Table 35: Semantic Prosodic Profiles of Booster + Adjectives

Booster +

1R adj Semantic

Prosody

so too very

Frequency Percentage Frequency Percentage Frequency Percentage

LOCNESS

(361,054)

Positive 89 66,91 62 63,91 151 44,67

Neutral 11 8,27 9 9,27 78 23,07

Negative 33 24,81 26 26,80 109 32,24

KTUCLE

(709,748)

Positive 269 68,62 237 69,09 623 61,31

Neutral 26 6,63 39 11,37 149 14,66

Negative 97 24,74 67 19,53 244 24,01

TICLE

(223,449)

Positive 118 71,08 49 63,63 178 48,90

Neutral 13 7,83 9 11,68 86 23,62

Negative 35 21,08 19 24,67 100 27,47

73

The reference corpus LOCNESS reveals that the three boosters similarly have a high

frequency of positive semantic prosodic profile and ‘ very important’ is the first most common

positive adjective collocation in three corpora. It is explicitly seen in the findings that the semantic

profiles of very in LOCNESS have a mixed nature in use since there is not a substantial difference

among percentages. The positive semantic profile of very constitutes 44% of whole while negative

prosodic percentage is 32% and neutral is 23%. The frequencies of very in TICLE is relatively

close to results in LOCNESS. However, in KTUCLE, the EFL learners tend to use very in positive

semantic orientation with a percentage over 60%. The adjectives having negative and neutral

prosodies are used less when compared to positive ones. The tertiary level EFL learners prefer very

as it can be simply combined with adjectives with various meanings. For having a broader

collocational range, very is highly exploited by language learners when compared to other boosters

or maximizers.

The booster so is the second most common booster preferred by the participants in three

corpora as illustrated in Table 35. The positive, negative and neutral semantic usage percentages of

so in native corpus and non-native corpora are very close to each other. The positive prosodic

usage of so is similarly high in each corpus with the percentage ranging between 67 and 71%.

However, this booster cannot be labeled as having a positive prosody because different ranges of

adjectives are likely to be used properly in combination with so. Some common examples to

adjective collocations of so having positive meaning are ‘important’, ‘useful’, and ‘easy’; those

having negative meanings are ‘hard’, ‘bad’, and ‘difficult’; and those having neutral meanings are

‘different’, ‘general’ and ‘little’.

Both in native corpus and non-native corpora, too is the less frequent one among other

boosters under investigation. The results seemingly imply that this booster mostly tends to intensify

adjectives carrying positive meanings. The percentages of too in LOCNESS go parallel with the

frequency in TICLE. Table 35 indicates that these two corpora have a positive semantic profile

with the percentage of 63% while KTUCLE has the percentage of 69%. However, these findings

tell us little about its underlying semantic profile. ‘Too many’ and ‘too much’ are the two most

common adjective collocations of too in all corpora. The total frequency of ‘much’ and ‘many’

constitutes 90% (f = 211) of whole positive adjectives in KTUCLE (f = 232). In LOCNESS,

‘much’ and ‘many’ compose 66% (f = 41) of whole positive adjectives in combination with too (f =

62), while they are exposed of 73% (f = 36) of positive ones in TICLE (f = 49). However, when the

concordance lines are analyzed in depth, it is clearly seen that ‘too much’ or ‘too many’ are highly

used for expressing exaggeration or negation. Although ‘many’ and ‘much’ seem to be adjectives

with positive attitudes, a negative association can be revealed when they collocate with too in

certain usage patterns such as ‘too much / many that’. Such occurrences will be analyzed in the next

pattern analysis section.

74

4.4. Pattern Analysis of Amplifiers

In this part, the most frequent usage patterns of amplifiers in non-native corpora KTUCLE

and TICLE are analyzed in comparison with those in LOCNESS based on the patterning labels of

Hunston and Francis (2000) in their book titled Pattern Grammar. In this particular research, a

pattern is regarded as “the combination of both grammar and lexis of a given node word, which

carries a specific meaning, and to realize certain function” (Wang 2017: 51). Adopted from Wang

(2017), the labelling of adjective intensification is abbreviated and expressed as ‘INT-adj’ which

refers to ‘intensifier + 1R adjective’ collocation. Intensifier (INT) is used as a representation for all

target amplifiers. As the focus of the study, the coding of intensifier is shown in capital letters

while other elements or word classes are in lower case letters. The elements representing an actual

word such as prepositions are coded in italics (Hunston and Francis, 2000: 33).

4.4.1. Usage Patterns of Maximizers

Seven maximizers totally, completely, absolutely, entirely, fully, utterly and perfectly in

native and non-native corpora were analyzed in terms of their patterning features. Different types

of maximizer plus adjective collocations were found in three corpora. However, it was observed

that most of the maximizer patterns were not abundantly used by both native speakers and non-

native learners. There are three common types of maximizer plus adjective collocation patterns

among seven types in three corpora. In its simplest form, the first pattern is intensifier + adjective

following a link verb which is abbreviated as ‘v-link INT adj’. Another major usage of maximizers

is negative form of the first pattern that is ‘v-link not INT adj’. The other frequent usage pattern is

intensifier + adjective clause modifying a noun labelled as ‘INT adj n’. Additionally, there are

other patterns such as ‘V int adj’, ‘modal v-link INT adj’, ‘v n/pron INT adj’ and independent use

of ‘INT adj’ which were not frequently used in native and non-native corpora. Therefore, these four

pattern types having a low percentage are categorized as ‘other patterns’ in Table 36.

Table 36: Overall Percentages of Maximizer Patterns

Maximizers

(INT-adj patterns)

LOCNESS

(361,054)

KTUCLE

(709,748)

TICLE

(223,449)

Frequency Percentage Frequency Percentage Frequency Percentage

v-link INT adj 41 66,12 53 70,66 12 44,40

INT adj n 14 22,58 12 16,00 6 22,22

v-link not INT adj 2 3,22 5 6,66 7 25,92

Other Patterns 5 8,06 5 6,66 2 7,40

TOTAL 62 100 75 100 27 100

75

According to the findings, most frequently used maximizer patterns in two learner corpora

are similar to ones in native corpus. In all three corpora, the first three patterns have the highest

percentages despite of the differences regarding the proportions. The ‘v-link INT adj’ is the

dominant pattern in LOCNESS, KTUCLE and TICLE. This pattern composes 70% of overall

usage in KTUCLE and 66% in LOCNESS. It is clearly seen that maximizers as modifying

adverbials preceding adjectives were mainly used for a predicative function. A predicate in a

sentence is thought to contain a verb explaining the action along with modifiers and other

complements. The concordance lines retrieved from three corpora under scrutiny are given below

to exemplify this pattern:

Pattern 1: v-link INT adj

…as you get older. They are skills that are absolutely essential in community organizations, in

(KTUCLE)

On the other hand, they can’t be equal as they’re totally different. First of all, their roles are (TICLE)

Additionally, the statistics show that the second pattern ‘INT adj n’ is also preferred by native

speakers and non-native learners with almost the same percentages. In this pattern, maximizers

were used as attributives accompanying with adjective to intensify noun as shown in the examples

below:

Pattern 2: INT adj n

…and you can communicate with people in a completely different environment and you

cannot (KTUCLE)

…everyday of the year, hundreds of fully conscious animals are scalded, beaten or (TICLE)

The negative form of the first pattern ‘v-link not INT adj’ is also seen in three corpora.

However, the learners have a tendency to use this type of negative pattern more than native

speakers. One reason is that native speakers use various negation forms (e.g. no, nobody, no one,

no longer, nothing, never, neither, nor) instead of negative maximizer patterns including not.

Another reason is that learners may be lack of awareness concerning semantic prosody, thus they

may refrain from using various negative maximizer patterns to express judgments.

… for rail transport, and as their problems are completely different in most respects, it must be clear

(LOCNESS)

…only should we study it should be given as an entirely separate discipline. Joseph Zaitchik,

editor (LOCNESS)

76

Pattern 3: v-link not INT adj

Yes, we use very a lot yet we are not totally dependable. In my opinion, if we have not the

(KTUCLE)

…from the problems of the life maybe this is not totally true, but not totally wrong. And

again (TICLE)

The total number of maximizers used in three corpora is not high in numbers; therefore, their

raw frequencies (or exact number of occurrences) can be shown in distinct tables in order to see

different usage patterns of each node words. It can be concluded there are some slight differences

in terms of usage. Table 37, Table 38 and Table 39 illustrate the overall occurrences of maximizer

+ adjective patterns in LOCNESS as reference corpus and KTUCLE and TICLE as learner corpora

under investigation:

Table 37: Typical patterns of ‘INT-adj’ in LOCNESS

LOCNESS (361,054) completely absolutely totally entirely fully perfectly utterly

1 v-link INT adj 10 4 10 5 3 9 -

2 v-link not INT adj - - 1 1 - - -

3 INT adj n 3 1 1 2 2 5 -

4 v INT adj - - - - 2 - 1

5 modal v-link INT adj 2 - - - - - -

6 v n/pron INT adj - - - - - - -

7 INT adj - - - - - - -

TOTAL 15 5 12 8 7 14 1

In LOCNESS, the first pattern has the highest percentage similar to learner corpora. Even

perfectly, which is scarce in learner English, is abundantly used in the pattern of ‘v-link INT adj’.

Completely and totally are similarly the most frequent maximizers in LOCNESS. Negative

predicative forms are not widely preferred by native speakers. That is to say, they often express

their judgments by adding negative prefixes to adjectives or using other items having negative

meaning such as never, no, nobody etc.

…the scientists involved, they are by no means totally blameless. I suggest the alternative

opinion (LOCNESS)

…of universal guilt because no one can be completely innocent. Camus raises the idea that

even Jesus (LOCNESS)

… the case, then the human brain will never become fully redundant… There has

(LOCNESS)

… ‘of making the best of it’. Caligula is not a totally abhorrent character in this play. Camus

gives (LOCNESS)

77

Table 38: Typical patterns of ‘INT-adj’ in KTUCLE

KTUCLE (709,748) completely absolutely totally entirely fully perfectly utterly

1 v-link INT adj 16 19 12 3 1 1 1

2 v-link not INT adj 3 - 1 1 - - -

3 INT adj n 7 1 1 1 1 1 -

4 v INT adj 1 1 1 - - - -

5 modal v-link INT adj 1 1 - - - - -

6 v n/pron INT adj - - - - - - -

7 INT adj - - - - - - -

TOTAL 28 22 15 5 2 2 1

KTUCLE, as the local learner corpus, is larger than other corpora concerning token number,

but many of the maximizer patterns are not much in this learner corpus. Completely, absolutely and

totally are the most common maximizers. Although completely most often appears having a

positive meaning in native corpus, the non-native students are more likely to use this maximizer in

negative sentences. Completely is also highly used in the third pattern.

Table 39: Typical patterns of ‘INT-adj’ in TICLE

In TICLE, the second learner corpus of the research, completely is the most frequent

maximizer and it is also used in negative predicative form similar to the findings in KTUCLE. Not

surprisingly, there is no occurrence of perfectly and utterly. Strikingly, the last two patterns in the

table ‘v n/pron INT adj’ and independent use of INT + adjective are only found in TICLE despite

of being a learner corpus. These two rare maximizer patterns occur with the use of absolutely as

shown in following concordance lines:

Pattern 6: v n/pron INT adj’

The conception of ‘ABORTION’. They may feel it absolutely compulsory to let the infant

off to get rid of (TICLE)

TICLE (223,449) completely absolutely totally entirely fully perfectly utterly

1 v-link INT adj 7 1 2 1 1 - -

2 v-link not INT adj 3 - 3 - 1 - -

3 INT adj n 1 - 1 - 4 - -

4 v INT adj - - - - - - -

5 modal v-link INT adj - - - - - - -

6 v n/pron INT adj - 1 - - - - -

7 INT adj - 1 - - - - -

TOTAL 11 3 6 1 6 0 0

78

Pattern 7: INT adj’

… a life is gone away and you want to go back. Absolutely impossible! So, how are you

going to keep this (TICLE)

4.4.2. Usage Patterns of Boosters

The three most frequently used boosters in English language, respectively so, too and very,

are examined in terms of their usage patterns in non-native corpus and learner corpora. It is

obviously seen that there is a strong predominance of boosters in the category of amplifiers. The

findings show that they are quite more than maximizers in three corpora (see Table 20). For this

reason, the frequency of each booster + adjective pattern is not counted one by one as it is done in

the pattern analysis of maximizers. Instead, the raw frequencies of each target booster are given

along with their percentages by focusing on differences and similarities in terms of typical patterns

and functions between native speakers and learners of English.

KTUCLE is the larger corpus in size; thus, three selected boosters are normally higher than

those in other two corpora. In all corpora, the order of most frequently boosters are the same,

respectively very, so and too. The analysis of three corpora revealed that there are not many

outstanding differences concerning the types of booster + adjective patterns. However, native

speakers and EFL learners significantly differ not in using various types of booster patterns but in

frequency of their occurrence. Randomly selected concordance lines exemplifying common pattern

varieties of each target booster + adjective combinations are retrieved from three corpora and will

be treated separately in depth under following subsections.

4.4.2.1. Very

Very is one of the most frequently encountered English amplifiers either in spoken or written

language. According to Lorenz (1999: 64), “very is the booster par excellence. It is highly versatile

in its collocability and combines almost freely with adjectives.” According to Table 20, nearly 60%

of total boosters in native corpus is composed of the booster very. The percentage of very in

LOCNESS is almost similar to that of TICLE with 60.36. KTUCLE has the highest frequency of

very, but its percentage of the whole is under 50. The concordance lines given below present

typical pattern types of booster combinations with the use of very:

Pattern 1: v-link INT adj

…good more than harm. Primarily, internet is very important in our daily lives as well as in

(KTUCLE)

…or not, but I do know that life for some people is very difficult and unless someone can really

feel (LOCNESS)

79

…immediately. The system in education is very strict, it uses the student only as a model.

(TICLE)

Pattern 2: INT adj n

…of the instant noticing. You can also see very beautiful places especially overseas, which

(KTUCLE)

…so many inventions in the 20th century and had very large impacts on our lives. They

changed our (TICLE)

Pattern 3: v-link INT adj for n/pron

…get used to live alone, living with a child is very hard for them. Secondly, they don’t

supply life (KTUCLE)

…best decision for the parents to divorce. It is very hard for the children to be witness to

their (TICLE)

Pattern 4: v-link INT adj to inf

…of treatment methods and medicines are very important to destroy these diseases. In this

(KTUCLE)

on the screen of your computer. Nowadays, it is very popular to find friends by the help of

computers (TICLE)

Pattern 5: modal v-link INT adj

…want assurance from someone eye contact can be very important in getting your thoughts

across. When (KTUCLE)

…there is an immediate event or warning it can be very good. They can reach you wherever you

are, but (TICLE)

Pattern 6: v INT adj

…is attack to animal rights. Scientists have very good reason for do it. To save the human life is

(KTUCLE)

…may be illogical, rubbish way which may seem very reasonable at first. A person is born alone,

(TICLE)

…bring up the point that coal mining is a very dangerous job. Coal miners can contract lung

(LOCNESS)

… nuclear power being so new and different, it is very scary for people. In order for these

people to be (LOCNESS)

…where relativism reign supreme. It would be very difficult to impose such an ethic on members of

(LOCNESS)

…to Hamlet’s uncle, Claudius. Hamlet can be very clever when it is necessary. When he suspects

(LOCNESS)

Again at peak times, trains can become very cramped with very few facilities and often (LOCNESS)

80

Pattern 7: v-link not INT adj

…or friends help us but a teacher safe us. It is not very complex. Sometimes we need to talk just a

(KTUCLE)

…like everything in our life television is not a very bad thing or not a very good thing in people’s

(TICLE)

The present investigation conducted on a native corpus and two learner corpora indicate that

there are seven above-mentioned common patterns of booster + adjective collocations. In addition

to these typical patterns appearing in all corpora with different proportions, there are some other

patterns which are not abundant but have some relatively significant nuances. It is apparent that

there are much different types of pattern in native corpus. For instance, in native corpus, some

examples to ‘v something INT adj’ pattern are found (e.g. find something very difficult to take).

Besides, ‘v-link prep INT adj n’ combinations are also encountered (e.g. under very fierce

competition). In LOCNESS, ‘INT adj n’ pattern is also found in the form of subject at the initial

position of sentence (e.g. Very little freight is transposed by rail these days.). The verb collocations

on the left are more likely to be varied in LOCNESS such as find, seem, offer, become make, feel,

have while in learner corpora verbs such as make, become, and have are observed. Different

negative forms are not preferred much with this adjective modifier. Patterns like ‘there v-link INT

adj’ and ‘this/that v-link INT adj’ are widely used in each corpus.

4.4.2.2. So

So is the second booster having the high-frequency adjective combinations in the present

study. So has the percentage of 34 of total booster use in KTUCLE while in TICLE so is employed

with the proportion of 27 percent (see Table 20). On the other hand, so composes nearly half of the

boosters in reference corpus. There are five typical patterns of so + adjective combinations in

LOCNESS, KTUCLE and TICLE. As in other amplifiers, the first two patterns, that are ‘v-link

INT adj’ and ‘INT adj n’, are dominantly observed with the use of so. The form of ‘v-link INT adj

that’ is another common pattern peculiar to the use of so + adjective, and the EFL learners’ usage

frequency of this pattern is found to be no fewer than the native speakers. The fourth booster

pattern accompanying to-infinitive is also preferred in both native corpus and two learner corpora.

Here are the concordance lines randomly extracted from three corpora:

Pattern 1: v-link INT adj

…a lot of disaster. Every people know that it is so harmful but they don’t want to believe

it’s (KTUCLE)

…role has evolved. In the constitution it is not very clear who exactly in charge. Some articles

(LOCNESS)

…in many different places because travel is so easy. The last area I’m going to touch on is

(LOCNESS)

81

…right to decide his own death. His children are so emotional and selfish. Because they

think (TICLE)

Pattern 2: INT adj n

…our mental process will work. Some may say ‘I am so sociable person and I learned a lot

of things to (KTUCLE)

…people. Although giving these two causes is so selfish attitude, the human being is only

think (TICLE)

Pattern 3: v-link INT adj that

…that situation was proved in the past. It is not so bad that death of animals, in the

environment (KTUCLE)

…a great effect on the child’s character. It is so obvious that violence has so many bad

effects (TICLE)

Pattern 4: v-link INT adj to inf

…have an enjoyable time. However, recently it is so rare to see anybody visiting his/her

friend or (KTUCLE)

…arrived at this point in his ongoing life, it is so hard to turn back. Maybe you don’t really

want to (TICLE)

Pattern 5: v INT adj

…, he went on. As she was telling to me felt so bad. She said he suffered much while he

was dying (KTUCLE)

…think that computers are not beneficial. It has so many advantages that I cannot mention

all of (TICLE)

Surprisingly, a widespread pattern in learner corpora is ‘v-link INT adj for n/pron’ where the

adjective is followed by for to signify someone or something ‘relating to’ or ‘connected with’ the

intensified thing. However, in LOCNESS, such usage type is not found.

Pattern 6: v-link INT adj for n/pron

…every time. First of all, family life is so important for a student. If a student’s family

(KTUCLE)

…each stage we take in life, we need money. It is so vital for people that one feels that

she/he can (TICLE)

The EFL learners tend to be lack of variety in forming negative sentences with boosters, as

well. They only prefer using not with link verbs to express negation in intensification, while native

speakers use other negative words.

…and authority. By showing how the networks have so little regard for that status that the

(LOCNESS)

...state of mind, health, and body. Disorder so severe that there is such a strong (LOCNESS)

...Hoederer in a bad light and the fact that he takes so long to carry out the act is because he’s

not (LOCNESS)

…jobs with good salaries, they may then become so preoccupied with their job and success,

that (LOCNESS)

82

Pattern 7: v-link NEG INT adj

Some frequent verbs collocating with boosters in learner corpora are become, feel, have,

know, and there are a few occurrences of love, need and provide in KTUCLE. Despite of being not

many in numbers, the verbs preceding so + adjective combination in TICLE contain give, have,

make, take and become. In LOCNESS, there are a variety of verbs collocating with boosters:

become, have, seem, spend, try, take, endure, and make. The modals such as must, can, could, will,

and should are also used in the pattern of ‘modal v-link INT adj’.

Lorenz (1999: 70) stated that “where there is no basis of comparison, the boosting function of

so is even more apparent, mostly in connection with emotive predications” (e.g. It looked so brutal

and disgusting). In fact, very is a more commonly used booster in English, but so is intentionally

preferred in some certain cases bearing correlative function. It is noticeable that ‘so much’ and ‘so

many’ are the high frequency combinations which can be proceeded immediately by ‘that clause’.

‘Much’ and ‘many’ can be regarded as adjectives in the scope of this study since they are denoting

quantity. All three groups are found to employ the patterns of ‘v-link INT adj that’ or sometimes

‘v-link INT adj to inf’ by using ‘so much’ or ‘so many’. Furthermore, the use of so plus adjective

in the function of a subject at initial position in a sentence structure is also encountered a few times

in learner corpora (e.g. So many people use the Internet for work.). The findings show that ‘so

many’ (LOCNESS f = 40, KTUCLE f = 57, TICLE f = 48) is more frequently preferred over ‘so

much’ (LOCNESS f = 19, KTUCLE f = 46, TICLE f = 21) in all corpora. However, it can be noted

that EFL learners have difficulty in writing grammatically correct form of countable and

uncountable nouns. They have an inclination to use ‘so much’ with countable things and ‘so many’

with vice versa. Even, in learner corpora, there are some misuses of plural and singular forms of

nouns. Turkish EFL learners often tend to forget adding –s to regular nouns to make them plural.

Most probably, this can be derived from the transfer of Turkish language, in which plural suffixes

(-ler or -lar) are not added to nouns when used with certain indefinite adjectives (e.g. birkaç, az,

biraz, çok, birçok, her, herhangi bir, hiçbir). The concordance lines below give examples to

improper use of many and much:

…the house, independently, as there are not so much children to look after. And if there are

(TICLE)

…it becomes like this every summer and we lost so much forests like this way every year.

Finally (KTUCLE)

…in most of the research. As important, there are so many study on genetics. Moreover, it

also (KTUCLE)

…society. A university in each city can provide so much things which are seem irrelevant

about (KTUCLE)

…compensation for services rendered is never so simple as remitting a predetermined salary

(LOCNESS)

…family unit. Family members are no longer so dependent on one another. They can go

elsewhere (LOCNESS)

83

…convenience to our life. Scientifically so many thing has changed that 50 years ago some

(KTUCLE)

…women but also men are use cosmetic products. So much product should be test again

before be our (KTUCLE)

4.4.2.3. Too

Too is the third most-frequent amplifier in current study. According to Table 18, LOCNESS

and KTUCLE have the similar percentages in using too as a booster although KTUCLE is almost

two times larger than LOCNESS in size. With the proportion of 12%, too is less frequent in TICLE

when compared to other two corpora. There are some common pattern clusters of too shared by

both native speakers and learners of English, which are ‘v-link INT adj’, v-link INT adj n’ and ‘v-

link INT adj for n/pron’. In addition, there is a major type of pattern with too often encountered in

all corpora: ‘v-link INT adj to inf’. The remaining patterns include booster intensification with

modals and verbs on the left position.

Pattern 1: v-link INT adj

…even though the distance between two houses was too distant, their neighbour, in village

they (KTUCLE)

…want to study for the exam, if the questions are too difficult, it they can only be answered

by (TICLE)

Pattern 2: INT adj n

For instance, in middle school, I have too much homework and school projects. I couldn’t

(KTUCLE)

… will help me; Professors deserve it because of too many assignments, poor teaching, and

unfair (TICLE)

Pattern 3: v-link INT adj for n/pron

…to move finger. Moreover, especially it is too important for women because internet

provides (KTUCLE)

…in order to have much money it means that you are too ambitious for money. Money

ambitious is not a (TICLE)

Pattern 4: v-link INT adj to inf

... can work outside at the same time. Of course, it is too hard to overcome this situation

(KTUCLE)

As well as the pointlessness of fox hunting it is too brutal on the now endangered wild fox

(LOCNESS)

…flaws, but his lack of respect for others and too much pride in himself lead Hamlet to his

(LOCNESS)

…initiative. Taking Richard away now would be too traumatic for him, or for that matter any

four (LOCNESS)

…in the ring as is sometimes the tragic case. It is too easy to allow emotion to control the

argument (LOCNESS)

84

…and he learns that this illness is very bad and too difficult to cure. Should the doctor

decide to (TICLE)

The pattern 2 ‘INT adj n’ is widely used booster pattern in both native and learner corpora.

Actually, boosters intensifying scalar words frequently collocate with ‘much’ and ‘many’. The

findings confirm that in three corpora, ‘much’ is found the first frequent collocation of too

(LOCNESS f = 23, KTUCLE f = 182, TICLE f = 29). As encountered in the collocations of ‘so

many’ and ‘so much’, non-native learners are observed to have problems in countable/uncountable

nouns placed in the collocation of ‘too much’ and ‘too many’.

…were used to save the world. But there are too much animals killed by experiments and I

could (KTUCLE)

As it is known, it youth period people have got too much problems and they seek away to

solve their (TICLE)

Additionally, the pattern ‘v-link INT adj to inf’ is found among the most common types of

adjective intensification with the head of booster too in all groups of corpora. Another version of

this pattern is ‘v-link INT adj to be v-ed’ which is less frequently observed in learner corpora. In

TICLE, there is just one concordance line containing this type of usage pattern; however in

KTUCLE, no such occurrence has been discovered. In LOCNESS, there are five occurrences in

this passive form. This result may be stem from the learners’ inadequate grammatical and lexical

knowledge of L2 which leads them to rely much on some certain intensifier patterns in written

production.

Pattern 5: v-link INT adj to be v-ed’

I put myself into a patient’s shoes who is too ill to be cured and the parents decide on

euthanasia (TICLE)

There are some common verbs collocates with INT + adj on the left position in the pattern of

‘v INT adj’. Most frequent common verbs used with adjective intensification are become, get, have,

take, give, seem, etc. in all three corpora. Even, EFL learners have been found to use different verbs

such as waste, use, spend, need, earn, want, cost, pay in a context where the subject is about

‘money’. However, this cannot be regarded as an indicator of a profound knowledge in L2. The

native speakers, on the other side, have an inclination to use a variety of different adverbs with

booster too such as all, far, almost, already, much, which is not seen in learner corpora (e.g. far too

many, all too much etc.) This is certainly a skill peculiar to the native speakers who are good at

collocating different words to intensify their message.

…believes that the responsibility of freedom is too great to be faced alone, advocates the

(LOCNESS)

… up to £9 million on any normal lottery draw) was too much to be given to just one party.

The recent (LOCNESS)

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CONCLUSION AND SUGGESTIONS

As previously mentioned, the present corpus-based research carried out an investigation to

reveal semantic prosodic awareness of Turkish EFL learners at tertiary level on the use of English

intensifiers in expository argumentation. For this purpose, the methodology of the research mainly

adopted the approach of Granger’s (1998) Contrastive Interlanguge Analysis by nature and

compared one native corpus with two native corpora in order to reveal typical patterns of native

speakers and EFL learners concerning the usage of intensifiers in written production. Although

intensifier was used as an umbrella term, the aim of the study was to investigate the overall

frequency distribution of amplifiers in combination with adjectives as well as their patterning and

semantic prosodic features. Based on the intensifier classification of Quirk et al. (1985), the

analysis was conducted in two subcategories as maximizers and boosters. The high frequent

maximizers and boosters with their adjective collocations were analyzed in depth. Adjective

intensification was intentionally entailed for the analysis because in practice such degree adverbs

strongly collocate with adjectives (Kennedy, 2010: 240). The target maximizers were absolutely,

completely, entirely, fully, perfectly, totally, and utterly while the boosters were so, too, and very.

The reference corpus of the study is LOCNESS (Louvain Corpus of Native English Essays)

that is consisted of 361,054 words compiled from the essays of native speakers. The local learner

corpus is KTUCLE with 709,749 tokens composed of the essays written by tertiary level EFL

students at Karadeniz Technical University. As the second learner corpus, TICLE (Turkish

International Corpus of Learner English) contains 223,449 words extracted from argumentative

essays by Turkish adult EFL learners. These three corpora were compared with the use of a

concordance tool called Sketch Engine. On this online concordancer, the raw frequencies of each

target amplifier and their normalized values were measured automatically. Since three corpora

under investigation differ in size, normalized frequency and Log-likelihood scores are of high

importance to make a comparison between the usage frequencies and overuse and underuse levels.

The starting point of the current study was to investigate the semantic prosodic awareness of

Turkish EFL learners on the use of intensifiers in their written essays. With this in mind, the

analysis of the study was divided into three main sections in search for overall frequency

distribution of amplifiers in native and non-native corpora, their overuse and underuse levels, and

semantic prosodic nature of amplifiers and their usage patterns as well. Initially, the overall

distribution of amplifiers plus adjectives both in native corpus and non-native corpora were

retrieved to make inference on their usage. It is clearly seen that boosters outnumbered maximizers

86

to a great extent. In this regard, Quirk et al. (1985) stated that “while maximizers are a restricted

set, the class of boosters is more open-ended” (as cited in Baker, 2010: 113). The total number of

boosters is 2926, and maximizers are 164. The raw frequency of maximizers in the reference

corpus is 62, while in KTUCLE, at least two times larger than LOCNESS and even the largest of

all, the frequency of maximizers is 75. TICLE, as the smallest learner corpus in the study, has only

27 maximizers in total. When normalized per million with the following formula (Standardized

Frequency = Raw Frequency x 1.000.000 / Corpora Content), LOCNESS is found to have a

standard frequency of 17,17 while KTUCLE has 10,56. It can be inferred that the usage of

maximizer collocations by non-native participants in KTUCLE is slightly less than native speakers.

However, it was observed that only three of the maximizers such as absolutely, completely and

totally are commonly used by EFL learners, while they underuse perfectly, and never have a

tendency to use utterly. The results of the frequency analysis demonstrate that Turkish non-native

learners of English at tertiary level use rather limited range of maximizers some of which such as

completely and totally are most often used by native speakers. The excessive reliance of EFL

learners on certain types of maximizers may be resulted from their restricted command of

vocabulary and language proficiency level. Another possible explanation can be that the students

are quite familiar with the frequent maximizers and pre-fabricated or prefixed collocations such as

‘totally wrong’, ‘absolutely necessary’, and ‘completely different’ that may possibly appear much

in English learning materials, books and academic writing. The remaining maximizers such as

entirely, fully, perfectly, and utterly are not preferred by non-native learners, and surprisingly most

of them are not also widely used by native speakers instead of perfectly.

Boosters, on the contrary, are highly exploited by tertiary level Turkish EFL learners. The

findings revealed that KTUCLE has the highest frequency of the boosters very, so and too, while

TICLE and LOCNESS are quite similar in terms of their percentages. Very is the most frequent

booster in three corpora. Very is heavily overused by non-native speakers “in overall occurrences

as well as adj-int function” (Lorenz, 1999: 30). It gets confirmed through the higher use of very that

“learners prefer using ‘all-round amplifiers’ or ‘safe-bets’ strategies, especially very, to minimize

errors” (Xiaohua and Haihua, 2007: 759). That is to say, on account of having limited

phraseological skills, foreign language learners refrain from using complex or unfamiliar phrases

so as to avoid making mistakes in language production. Very is an intensifier than can be easily

employed by EFL learners in various usage patterns without any semantic prosodic concern.

In order to distinguish typical collocation patterns of booster intensification, which are

apparently high in number, the top ten most frequent common boosters in all groups of corpora

were analyzed. For each booster, the reference corpus was compared separately with KTUCLE and

TICLE based on Log-likelihood scores to identify overuse and underuse levels of EFL learners.

‘Very important’ is found to be the highest overused collocation both in KTUCLE and TICLE

87

when compared to the LOCNESS. The common underused booster + adjective intensification

patterns in KTUCLE and TICLE are ‘very few’ and ‘very strong’.

So is the second highest booster in three corpora. Lorenz (1999: 70) stated that when there is

no basis of comparison, the boosting function of so becomes more apparent. As occurs with very,

‘so important’ is also overused by the participants in two learner corpora. Not surprisingly, ‘so

many’ and ‘so much’ are the widely-preferred booster collocation in two learner corpora; however,

they are also widely used by native speakers. Lastly, too is the least frequent booster among three

corpora. According to Lorenz (1999: 70), while modifying an adjective, too certainly has a function

to scale upwards unless used in negation, and he added that “in this boosting function, and in its

virtually unrestricted collocability, it resembles very, the booster par excellence.” ‘Too much’ is the

common overuse pattern in KTUCLE and TICLE. ‘Too many’ is also highly preferred by the

participants in two corpora, but no considerable difference was found between non-native corpora

and native corpus.

Semantic prosody, as the core of the study, was analyzed based on Stubbs’ (1996)

classification as positive, negative, and neutral. The target amplifiers in combination with

adjectives in native and non-native corpora were all examined in terms of their semantic prosodic

profiles. The results showed that completely and totally are the most common maximizers preferred

by EFL learners. Since these two near-synonymous maximizers have an underlying negative

association in meaning, they can be used in a similar manner. However, EFL learners are not fully

conscious of the bad semantic prosodic nature of completely. Besides, they have a tendency to use

totally with adjectives bearing positive meaning. This can be attributed to their inadequate

competence in pragmatics. Another striking result is that EFL learners are more likely to use

familiar types of maximizers for adjective intensification such as completely, absolutely, and

totally, and they underuse other intensifiers such as perfectly and utterly. According to Wang

(2017: 125), “L1 transfer plays an obvious role in causing the overuse, underuse and misuse of

certain intensifiers.” Additionally, boosters very, so and too can be freely and interchangeably used

for negative, positive and neutral attitudes. That is why, Turkish EFL learners tend to use boosters

much predominantly in writing than maximizers.

As a special focus of the research, the usage patterning of amplifiers were examined based

on the patterning labels of Hunston and Francis (2000). Adopted from Wang (2017), the labelling

of adjective intensification was abbreviated as ‘INT-adj’. The most common usage patterns of

amplifiers in three corpora are ‘v-link INT adj’, ‘INT adj n’, and ‘v-link not INT adj’ which are

basic patterns constructed in writing. The non-native speakers use negative maximizer patterns

including not more than natives, because the learners may lack of semantic prosodic awareness to

use intensifiers in negative pattern for expressing attitudes. However, native speakers use various

negation forms (e.g. no, nobody, no one, no longer, nothing, never, neither, nor). The EFL learners

88

also underuse some usage patterns such as ‘v-link TOO adj to be v-ed’ which is peculiar to native

use. In addition, EFL learners were found to be lack of variety in using different items such as far,

all, almost with intensifiers as native speakers do. There are also a great many link verbs preceding

adjective intensification in the control corpus. It was also observed that ‘many’ and ‘much’ are

predominantly used with so and too, but Turkish EFL learners have a difficulty in distinguishing

countable and uncountable nouns while using with intensification. This can be resulted from the

transfer of Turkish language, in which plural suffixes are not added to nouns when used with

certain indefinite adjectives. It can be concluded that all these features may stem from the learners’

inadequate grammatical and lexical knowledge of L2.

From a pedagogical point of view, the study draws the implication that grammatical

competence is not enough to be competent in a foreign language. Instead, foreign language students

should be competent in pragmatic skills to master the vocabulary in target language. Near-

synonymous words like intensifiers have potential to be a challenge for foreign language learners

due to the nuances they bear in meaning. Thus, they should pay much attention to their semantic

prosodic nature to have a proficiency in using such adverbials. The curriculum may not include

explicit teaching of intensifiers, but some pragmatic classroom activities can be designed for

foreign language learners to raise their level of awareness of semantic prosody.

Concerning the limitations, it can be stated the present study solely focuses on adjective

intensification (INT-adj) which is supposed to be more abundant in language use. The degree

adverbials intensifying verbs, adverbs, nouns, or prepositional phrases are beyond the scope of this

research. Besides, a narrow-downed category of intensifiers with a limited number is

predetermined for the analysis. The scope of the research, otherwise, would be more complex and

wider to be analyzed. However, according to Lorenz (1998: 53-54), to scale the quality of

intensification “it would not have been sufficient to search and retrieve a set of previously

identified intensifiers.” It is necessary to make an analysis of intensification as comprehensive as

possible “rather than checking the corpora for a pre-defined finite set.” Furthermore, the research

embarks on the interlanguage problems of non-native learners, analyzing how frequently they

overuse or/and underuse intensifiers in written English. The misuse of intensifiers by Turkish EFL

learners is not examined in depth because “the strength of learner corpus analysis lies in the

detection of patterns, not errors” (Lorenz, 1998: 9).

For further studies, a similar research may be conducted through two or more learner corpora

including participants from different language proficiency levels. In this way, the interlanguage

development features of EFL learners in using intensifiers can be compared and identified to what

extent they progress in terms of semantic prosodic behavior. Secondly, a corpus-based comparative

study can be carried out on female or male EFL learners focusing on gender preference in

employing intensification in academic writing. The usage of intensifiers by native speakers and

89

non-native learners in conversational settings can also be investigated to uncover which types of

intensifiers are more preferred in spoken discourse.

90

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APPENDICES

102

Appendix 1: Concordances from Sketch Engine showing maximizers in LOCNESS

103

104

Appendix 2: Concordances from Sketch Engine showing maximizers in KTUCLE

105

106

107

Appendix 3: Concordance from Sketch Engine showing maximizers in TICLE

108

Appendix 4: The Semantic Profiles of VERY + 1R adjectives in LOCNESS

POSITIVE NEGATIVE NEUTRAL

important

good

strong

popular

competitive

powerful

real

significant

successful

interesting

positive

young

happy

large

easy

impressionable

sensitive

clever

favourable

proud

influential

simple

effective

true

interested

much

magnanimous

credible

affective

idealistic

useful

self-concerned

unique

patient

resilient

helpful

careful

viable

profitable

beneficial

possible

ready

clear

safe

fair

great

respectable

amusing

rich

logical

new

aware

realistic

substantial

special

25

14

12

7

5

5

5

5

3

3

3

3

3

3

3

2

2

2

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

few

difficult

expensive

low

hard

dangerous

controversial

rare

subtle

boring

unpopular

weak

disturbing

thin

capricious

upset

scary

ignorant

depressed

misguided

confused

unsure

painful

doubtful

sceptical

harsh

violent

negative

complex

complicated

ugly

unfair

uncomfortable

unrealistic

angry

sick

fierce

cramped

unreliable

severe

damaging

contagious

wary

least

dependent

busy

stressful

overpopulated

devastating

alarming

melancholy

cruel

heated

timid

apprehensive

15

12

6

6

6

5

3

3

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

little

small

high

similar

likely

long

big

large

specific

basic

own

recent

first

same

common

different

masculine

soft

ethnocentric

functional

impersonal

authoritative

technical

raw

direct

stereo-typical

typical

grey

individual

personal

natural

wide

obvious

ironic

sound

11

5

5

5

4

4

3

3

3

3

3

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

109

exciting

exclusive

intense

patriotic

1

1

1

1

serious

debatable

1

1

TOTAL 151 TOTAL 109 TOTAL 78

110

Appendix 5: The Semantic Profiles of VERY + 1R adjectives in KTUCLE

POSITIVE NEGATIVE NEUTRAL

important

useful

good

easy

high

much

beneficial

necessary

happy

helpful

simple

popular

sensitive

significant

beautiful

attractive

interesting

effective

strong

successful

essential

careful

young

valuable

interested

enjoyable

clear

healthy

unique

reliable

powerful

excited

modern

substantial

awesome

proud

portable

noble

exciting

crucial

comfortable

pleasant

lucky

cheap

suitable

true

prestigious

admirable

imperceptible

grateful

hard-working

virtuous

fascinating

ambitious

superficial

245

66

33

21

17

11

11

10

9

9

8

7

7

7

7

6

6

6

6

6

5

5

5

4

4

4

4

4

3

3

3

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

dangerous

hard

harmful

difficult

bad

expensive

sad

few

heavy

tired

wrong

strange

thoughtless

old

remote

rare

useless

sorry

strict

angry

stressful

complex

painful

nervous

cruel

unhealthy

ill

low

poor

busy

dramatic

loud

vulnerable

inefficient

noisy

uncomfortable

notorious

unusual

unstable

exhausting

bitter

asocial

lazy

unnecessary

awful

negative

selfish

nonsense

weak

aggressive

risky

anti-social

deceptive

thin

critical

35

25

24

23

19

12

11

7

5

4

4

3

3

3

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

different

big

short

small

little

serious

common

large

huge

long

similar

normal

early

widespread

first

hot

general

broad

personal

natural

basic

present

conservative

direct

liberal

classic

technological

ordinary

near

close

deliberate

22

20

14

11

10

9

9

5

5

5

4

4

3

3

3

3

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

111

fashionable

knowledgeable

stout

extensive

harmless

decisive

reprehensible

sensible

joyful

handy

civilized

fond

meaningful

fantastic

profitable

alert

accurate

reasonable

brave

vital

cheerful

optimistic

fundamental

super

quick

necessary

precious

clever

emotional

great

nice

active

funny

available

positive

fast

silent

appropriate

real

social

possible

special

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

irresponsible

tragic

shy

minor

sorrowful

boring

merciless

dissuasive

1

1

1

1

1

1

1

1

TOTAL 623 TOTAL 244 TOTAL 149

112

Appendix 6: The Semantic Profiles of VERY + 1R adjectives in TICLE

POSITIVE NEGATIVE NEUTRAL

important

good

successful

useful

easy

simple

high

great

rich

much

interesting

careful

popular

beautiful

effective

young

handsome

helpful

surprising

clever

practical

clear

beneficial

invaluable

surprised

rightful

attractive

alluring

independent

intense

prestigious

nice

delicious

affective

colourful

ideal

comfortable

elegant

exciting

reasonable

skilful

experienced

essential

sweet

fast

cheap

powerful

emotional

happy

strong

necessary

many

sensitive

emotive

44

16

10

9

9

8

7

4

4

4

3

3

3

3

3

3

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

difficult

bad

hard

ill

serious

dangerous

low

harmful

old

lonely

strict

useless

expensive

cruel

wrong

embarrassing

distressing

faulty

sad

negative

fat

nervous

hopeless

complex

miserable

dirty

curious

thin

disturbing

merciless

poor

few

sick

tired

afraid

17

14

11

6

5

5

4

3

3

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

big

little

short

different

small

common

long

detailed

close

widespread

first

deep

general

obvious

ordinary

large

early

cold

15

15

14

12

6

5

4

2

2

2

2

1

1

1

1

1

1

1

TOTAL 178 TOTAL 100 TOTAL 86

113

Appendix 7: The Semantic Profiles of TOO + 1R adjectives in LOCNESS

POSITIVE NEGATIVE NEUTRAL

much

many

easy

high

great

important

adventurous

precise

self-interested

new

young

strong

23

14

5

4

3

3

1

1

1

1

1

1

late

costly

loud

busy

lazy

vague

long

old

bad

lethargic

traumatic

violent

low

difficult

big

large

5

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

wet

utopic

premature

minute

traditional

brutal

abstract

radical

apparent

cold

little

1

1

1

1

1

1

1

1

1

1

1

TOTAL 58 TOTAL 28 TOTAL 11

114

Appendix 8: The Semantic Profiles of TOO + 1R adjectives in KTUCLE

POSITIVE NEGATIVE NEUTRAL

much

many

important

sensitive

young

strong

easy

sensible

intense

ready

fast

careful

simple

high

useful

182

29

10

2

2

2

2

1

1

1

1

1

1

1

1

dependent

difficult

hard

less

expensive

dangerous

bad

complex

lazy

tired

few

scary

distant

extreme

regretful

critical

blind

remote

serious

weak

sad

26

7

4

3

3

3

3

2

2

2

2

1

1

1

1

1

1

1

1

1

1

late

long

early

big

hot

little

close

cold

short

small

23

3

3

2

2

2

1

1

1

1

TOTAL 237 TOTAL 67 TOTAL 39

115

Appendix 9: The Semantic Profiles of TOO + 1R adjectives in TICLE

POSITIVE NEGATIVE NEUTRAL

much

many

young

ambitious

smart

helpful

strong

simple

high

interested

useful

important

29

7

4

1

1

1

1

1

1

1

1

1

difficult

expensive

hard

long

few

strict

busy

insufficient

low

ill

complicated

6

2

2

2

1

1

1

1

1

1

1

late

systematic

materialistic

theoretical

big

5

1

1

1

1

TOTAL 49 TOTAL 19 TOTAL 9

116

Appendix 10: The Semantic Profiles of SO + 1R adjectives in LOCNESS

POSITIVE NEGATIVE NEUTRAL

many

much

great

simple

easy

important

eager

intent

funny

inexpensive

intense

full

quick

advanced

proud

emotional

positive

new

useful

powerful

righteous

popular

strong

reliant

immense

40

19

5

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hard

rare

low

difficult

preoccupied

embarrassing

undisciplined

sudden

addictive

uncommon

heated

tragic

unable

unreliable

severe

dependent

negative

old

expensive

bad

wrong

jaded

rampant

ambiguous

7

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

little

long

prevalent

general

different

5

3

1

1

1

TOTAL 89 TOTAL 33 TOTAL 11

117

Appendix 11: The Semantic Profiles of SO + 1R adjectives in KTUCLE

POSITIVE NEGATIVE NEUTRAL

many

much

important

happy

easy

useful

beneficial

lucky

strong

necessary

popular

most

simple

young

good

logical

cute

famous

innocent

remarkable

great

smart

enjoyable

reliable

beautiful

cheap

social

healthy

successful

effective

true

advantageous

swift

soft

skilful

non-violent

sociable

quality

wonderful

progressed

regular

glad

rich

excited

rational

sophisticated

quick

magnificent

clever

significant

clear

sympatric

powerful

certain

modern

full

57

46

44

9

8

7

6

5

4

4

3

3

3

3

3

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hard

harmful

bad

difficult

heavy

stressful

awful

sad

wrong

upset

depressed

disturbing

poor

busy

dangerous

dependent

lost

intolerant

provocative

intolerable

wicked

insensitive

sharp

unlucky

monotone

alarming

problematic

pathetic

hazardous

rough

slow

absurd

heartless

unacceptable

rare

deceptive

ridiculous

distant

exhausting

addicted

risky

wild

selfish

violent

sorry

less

cruel

nonsense

limited

boring

ill

expensive

9

9

8

7

4

3

3

3

3

2

2

2

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

normal

different

close

widespread

big

interbedded

routine

ironic

common

dry

little

small

wide

quiet

6

5

2

2

2

1

1

1

1

1

1

1

1

1

118

convenient

funny

preferable

real

1

1

1

1

TOTAL 267 TOTAL 99 TOTAL 26

119

Appendix 12: The Semantic Profiles of SO + 1R adjectives in TICLE

POSITIVE NEGATIVE NEUTRAL

many

much

important

easy

clear

good

effective

necessary

fresh

brave

sweet

ambitious

vital

fast

essential

free

emotional

new

strong

high

striking

useful

48

21

15

9

4

3

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

hard

weak

tired

cruel

bad

hurtful

odd

irresponsible

selfish

horrible

immoral

disabled

unhappy

disturbing

merciless

helpless

unnecessary

busy

insufficient

expensive

harmful

wrong

dangerous

low

ill

difficult

6

2

2

2

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

different

routine

colourful

natural

obvious

ordinary

tight

little

big

long

4

1

1

1

1

1

1

1

1

1

TOTAL 118 TOTAL 35 TOTAL 13

120

CURRICULUM VITAE

Neslihan KELEŞ was born in Trabzon in 1986. She is originally from Artvin. Her high

school education was between the years of 1997 and 2004 in Akçaabat Anatolian High School. She

was graduated from the Department of English Language and Literature and got an English

Language Teaching Certificate from the Faculty of Education at Karadeniz Technical University.

After graduation, she was granted as Comenius Language Assistant by Turkish National Agency to

work in a primary school in Italy for six months in 2009-2010 academic year. She has been

studying for a master degree in applied linguistics at Karadeniz Technical University. She also has

an undergraduate degree in International Relations from the Faculty of Open Education in Anadolu

University. She has been currently working as chief human resources officer in Eastern Blacksea

Development Agency in Trabzon since 2011.

She is married and mother of two daughters.